Overview

Brought to you by YData

Dataset statistics

Number of variables64
Number of observations716031
Missing cells17908872
Missing cells (%)39.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 GiB
Average record size in memory2.3 KiB

Variable types

Numeric22
DateTime8
Categorical19
Text9
Unsupported2
Boolean2
URL2

Alerts

protocol_id has constant value "8" Constant
protocol_name has constant value "Surface Temperature" Constant
protocol_model has constant value "SurfaceTemperature" Constant
protocol_association_name has constant value "surface_temperature" Constant
protocol_alt_name has constant value "Surface Temperature" Constant
protocol_investigation_area has constant value "Atmosphere" Constant
protocol_set_name has constant value "Surface Temperature" Constant
protocol_set_code has constant value "9808" Constant
developer_key_is_citizen_science is highly overall correlated with developer_key_name and 8 other fieldsHigh correlation
developer_key_name is highly overall correlated with developer_key_is_citizen_science and 7 other fieldsHigh correlation
homogeneous_site_long_length_m is highly overall correlated with homogeneous_site_short_length_m and 1 other fieldsHigh correlation
homogeneous_site_short_length_m is highly overall correlated with homogeneous_site_long_length_m and 1 other fieldsHigh correlation
organizationid is highly overall correlated with site_elevation_type and 10 other fieldsHigh correlation
sample_snow_depth_flag is highly overall correlated with site_is_citizen_science and 3 other fieldsHigh correlation
sample_snow_depth_mm is highly overall correlated with sample_surface_temperature_c and 1 other fieldsHigh correlation
sample_surface_temperature_c is highly overall correlated with sample_snow_depth_mm and 2 other fieldsHigh correlation
site_developer_key_id is highly overall correlated with developer_key_is_citizen_science and 7 other fieldsHigh correlation
site_elevation is highly overall correlated with site_true_elevation and 2 other fieldsHigh correlation
site_elevation_type is highly overall correlated with organizationid and 7 other fieldsHigh correlation
site_id is highly overall correlated with organizationid and 9 other fieldsHigh correlation
site_is_citizen_science is highly overall correlated with homogeneous_site_long_length_m and 11 other fieldsHigh correlation
site_latitude is highly overall correlated with organizationid and 5 other fieldsHigh correlation
site_location_source is highly overall correlated with site_elevation_type and 5 other fieldsHigh correlation
site_longitude is highly overall correlated with site_true_longitude and 3 other fieldsHigh correlation
site_true_elevation is highly overall correlated with developer_key_is_citizen_science and 9 other fieldsHigh correlation
site_true_latitude is highly overall correlated with developer_key_is_citizen_science and 16 other fieldsHigh correlation
site_true_longitude is highly overall correlated with developer_key_is_citizen_science and 16 other fieldsHigh correlation
site_true_point is highly overall correlated with developer_key_is_citizen_science and 27 other fieldsHigh correlation
st_id is highly overall correlated with organizationid and 11 other fieldsHigh correlation
sts_id is highly overall correlated with organizationid and 11 other fieldsHigh correlation
submission_developer_key_id is highly overall correlated with developer_key_is_citizen_science and 18 other fieldsHigh correlation
submission_elevation is highly overall correlated with site_elevation and 4 other fieldsHigh correlation
submission_id is highly overall correlated with developer_key_is_citizen_science and 13 other fieldsHigh correlation
submission_latitude is highly overall correlated with site_elevation_type and 6 other fieldsHigh correlation
submission_longitude is highly overall correlated with site_elevation_type and 6 other fieldsHigh correlation
surface_condition is highly overall correlated with site_true_latitude and 1 other fieldsHigh correlation
surface_cover_type is highly overall correlated with site_is_citizen_science and 4 other fieldsHigh correlation
user_type_description is highly overall correlated with site_true_latitude and 5 other fieldsHigh correlation
userid is highly overall correlated with organizationid and 7 other fieldsHigh correlation
usertype is highly overall correlated with site_true_latitude and 5 other fieldsHigh correlation
version is highly overall correlated with sample_snow_depth_flag and 4 other fieldsHigh correlation
version_id is highly overall correlated with developer_key_is_citizen_science and 9 other fieldsHigh correlation
surface_cover_type is highly imbalanced (56.9%) Imbalance
site_elevation_type is highly imbalanced (52.0%) Imbalance
site_developer_key_id is highly imbalanced (62.9%) Imbalance
site_is_citizen_science is highly imbalanced (99.9%) Imbalance
developer_key_name is highly imbalanced (62.9%) Imbalance
surface_condition has 131566 (18.4%) missing values Missing
submission_id has 625966 (87.4%) missing values Missing
sample_snow_depth_mm has 547828 (76.5%) missing values Missing
sample_snow_depth_flag has 691406 (96.6%) missing values Missing
version_id has 100398 (14.0%) missing values Missing
version has 100398 (14.0%) missing values Missing
site_version_activated_at has 100398 (14.0%) missing values Missing
version_date has 100398 (14.0%) missing values Missing
site_version_comments has 369536 (51.6%) missing values Missing
homogeneous_site_short_length_m has 153800 (21.5%) missing values Missing
homogeneous_site_long_length_m has 153819 (21.5%) missing values Missing
surface_cover_type has 163529 (22.8%) missing values Missing
instrument_type has 245047 (34.2%) missing values Missing
submission_comments has 706334 (98.6%) missing values Missing
submission_developer_key_id has 625971 (87.4%) missing values Missing
submission_access_code_id has 716031 (100.0%) missing values Missing
submission_latitude has 644866 (90.1%) missing values Missing
submission_longitude has 644866 (90.1%) missing values Missing
submission_elevation has 644866 (90.1%) missing values Missing
submission_point has 644866 (90.1%) missing values Missing
submission_data has 693424 (96.8%) missing values Missing
protocol_set_name has 644866 (90.1%) missing values Missing
protocol_set_code has 644866 (90.1%) missing values Missing
site_deactivated_at has 715409 (99.9%) missing values Missing
site_comments has 545001 (76.1%) missing values Missing
site_elevation_type has 484872 (67.7%) missing values Missing
site_nickname has 716031 (100.0%) missing values Missing
site_true_latitude has 715947 (> 99.9%) missing values Missing
site_true_longitude has 715947 (> 99.9%) missing values Missing
site_true_elevation has 715947 (> 99.9%) missing values Missing
site_true_point has 715947 (> 99.9%) missing values Missing
site_photo_measured_at has 621473 (86.8%) missing values Missing
site_photo_primary_thumb_url has 621477 (86.8%) missing values Missing
site_photo_primary_photo_url has 621477 (86.8%) missing values Missing
site_photo_photo_data has 621473 (86.8%) missing values Missing
version is highly skewed (γ1 = 26.9488975) Skewed
submission_access_code_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
site_nickname is an unsupported type, check if it needs cleaning or further analysis Unsupported
sample_snow_depth_mm has 142620 (19.9%) zeros Zeros

Reproduction

Analysis started2025-07-07 19:00:45.539923
Analysis finished2025-07-07 19:02:01.019841
Duration1 minute and 15.48 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

st_id
Real number (ℝ)

High correlation 

Distinct162932
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68781.083
Minimum1
Maximum167008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-07-07T15:02:01.053510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5442
Q123059
median58642
Q3111423
95-th percentile156426
Maximum167008
Range167007
Interquartile range (IQR)88364

Descriptive statistics

Standard deviation49997.923
Coefficient of variation (CV)0.72691387
Kurtosis-1.1432147
Mean68781.083
Median Absolute Deviation (MAD)40882
Skewness0.40068954
Sum4.9249388 × 1010
Variance2.4997923 × 109
MonotonicityNot monotonic
2025-07-07T15:02:01.094820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166969 9
 
< 0.1%
147750 9
 
< 0.1%
147686 9
 
< 0.1%
132452 9
 
< 0.1%
132422 9
 
< 0.1%
132421 9
 
< 0.1%
132420 9
 
< 0.1%
147698 9
 
< 0.1%
147704 9
 
< 0.1%
147712 9
 
< 0.1%
Other values (162922) 715941
> 99.9%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 3
 
< 0.1%
3 9
< 0.1%
4 9
< 0.1%
5 9
< 0.1%
6 9
< 0.1%
7 9
< 0.1%
8 9
< 0.1%
9 9
< 0.1%
10 9
< 0.1%
ValueCountFrequency (%)
167008 9
< 0.1%
167007 9
< 0.1%
167006 9
< 0.1%
167005 9
< 0.1%
167004 1
 
< 0.1%
167003 1
 
< 0.1%
167002 1
 
< 0.1%
167001 1
 
< 0.1%
167000 1
 
< 0.1%
166999 1
 
< 0.1%

site_id
Real number (ℝ)

High correlation 

Distinct5705
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90378.065
Minimum1264
Maximum381601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-07-07T15:02:01.135719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1264
5-th percentile5098
Q127840
median35139
Q3139596
95-th percentile306037
Maximum381601
Range380337
Interquartile range (IQR)111756

Descriptive statistics

Standard deviation94051.112
Coefficient of variation (CV)1.0406409
Kurtosis0.85860652
Mean90378.065
Median Absolute Deviation (MAD)26624
Skewness1.335377
Sum6.4713496 × 1010
Variance8.8456117 × 109
MonotonicityNot monotonic
2025-07-07T15:02:01.178013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3898 20676
 
2.9%
104218 20555
 
2.9%
35593 18392
 
2.6%
105966 14555
 
2.0%
27841 13622
 
1.9%
5144 13186
 
1.8%
27840 13091
 
1.8%
125549 11663
 
1.6%
35139 10912
 
1.5%
33276 10406
 
1.5%
Other values (5695) 568973
79.5%
ValueCountFrequency (%)
1264 336
 
< 0.1%
1510 1
 
< 0.1%
1649 1
 
< 0.1%
2000 6
 
< 0.1%
2329 18
 
< 0.1%
3286 81
 
< 0.1%
3298 411
 
0.1%
3363 5132
0.7%
3386 4671
0.7%
3426 162
 
< 0.1%
ValueCountFrequency (%)
381601 6
 
< 0.1%
381176 1
 
< 0.1%
381174 1
 
< 0.1%
381173 1
 
< 0.1%
381172 1
 
< 0.1%
381167 1
 
< 0.1%
381001 72
< 0.1%
380996 72
< 0.1%
380994 18
 
< 0.1%
380933 63
< 0.1%
Distinct131355
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
Minimum1999-04-13 16:12:00
Maximum2025-03-27 17:42:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T15:02:01.222092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:02:01.262885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

protocol_id
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.1 MiB
8
716031 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters716031
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 716031
100.0%

Length

2025-07-07T15:02:01.300682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:01.326110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8 716031
100.0%

Most occurring characters

ValueCountFrequency (%)
8 716031
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 716031
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 716031
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 716031
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 716031
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 716031
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 716031
100.0%

userid
Real number (ℝ)

High correlation 

Distinct1933
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21974569
Minimum-1
Maximum1.4827748 × 108
Zeros0
Zeros (%)0.0%
Negative153586
Negative (%)21.4%
Memory size6.1 MiB
2025-07-07T15:02:01.452345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q12549450
median9244353
Q324128001
95-th percentile98268451
Maximum1.4827748 × 108
Range1.4827748 × 108
Interquartile range (IQR)21578551

Descriptive statistics

Standard deviation30919311
Coefficient of variation (CV)1.4070497
Kurtosis3.0512222
Mean21974569
Median Absolute Deviation (MAD)9244354
Skewness1.8896207
Sum1.5734473 × 1013
Variance9.5600376 × 1014
MonotonicityNot monotonic
2025-07-07T15:02:01.493352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 153586
 
21.4%
2627792 23706
 
3.3%
6356348 20632
 
2.9%
20826653 18441
 
2.6%
2549450 17831
 
2.5%
7958830 17632
 
2.5%
12995898 16142
 
2.3%
2625390 10943
 
1.5%
12983336 10912
 
1.5%
2521381 10756
 
1.5%
Other values (1923) 415450
58.0%
ValueCountFrequency (%)
-1 153586
21.4%
11077 2
 
< 0.1%
11086 1
 
< 0.1%
11114 5
 
< 0.1%
11123 2
 
< 0.1%
352811 12
 
< 0.1%
383593 36
 
< 0.1%
383748 3
 
< 0.1%
387685 132
 
< 0.1%
398180 2
 
< 0.1%
ValueCountFrequency (%)
148277476 54
 
< 0.1%
148206535 288
< 0.1%
147703959 9
 
< 0.1%
147643291 6
 
< 0.1%
145840238 9
 
< 0.1%
145840193 9
 
< 0.1%
145840178 8
 
< 0.1%
145840053 9
 
< 0.1%
145839662 46
 
< 0.1%
145839647 57
 
< 0.1%

surface_condition
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing131566
Missing (%)18.4%
Memory size36.0 MiB
dry
409246 
wet
159726 
snow
 
15493

Length

Max length4
Median length3
Mean length3.026508
Min length3

Characters and Unicode

Total characters1768888
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdry
2nd rowdry
3rd rowdry
4th rowdry
5th rowdry

Common Values

ValueCountFrequency (%)
dry 409246
57.2%
wet 159726
 
22.3%
snow 15493
 
2.2%
(Missing) 131566
 
18.4%

Length

2025-07-07T15:02:01.530556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:01.552698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
dry 409246
70.0%
wet 159726
 
27.3%
snow 15493
 
2.7%

Most occurring characters

ValueCountFrequency (%)
d 409246
23.1%
r 409246
23.1%
y 409246
23.1%
w 175219
9.9%
e 159726
 
9.0%
t 159726
 
9.0%
s 15493
 
0.9%
n 15493
 
0.9%
o 15493
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1768888
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 409246
23.1%
r 409246
23.1%
y 409246
23.1%
w 175219
9.9%
e 159726
 
9.0%
t 159726
 
9.0%
s 15493
 
0.9%
n 15493
 
0.9%
o 15493
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1768888
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 409246
23.1%
r 409246
23.1%
y 409246
23.1%
w 175219
9.9%
e 159726
 
9.0%
t 159726
 
9.0%
s 15493
 
0.9%
n 15493
 
0.9%
o 15493
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1768888
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 409246
23.1%
r 409246
23.1%
y 409246
23.1%
w 175219
9.9%
e 159726
 
9.0%
t 159726
 
9.0%
s 15493
 
0.9%
n 15493
 
0.9%
o 15493
 
0.9%

organizationid
Real number (ℝ)

High correlation 

Distinct1217
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13820446
Minimum-5
Maximum1.4536451 × 108
Zeros0
Zeros (%)0.0%
Negative3182
Negative (%)0.4%
Memory size5.5 MiB
2025-07-07T15:02:01.582525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile75383
Q1239998
median394556
Q319359290
95-th percentile63367622
Maximum1.4536451 × 108
Range1.4536452 × 108
Interquartile range (IQR)19119292

Descriptive statistics

Standard deviation23034988
Coefficient of variation (CV)1.6667326
Kurtosis6.4905877
Mean13820446
Median Absolute Deviation (MAD)342449
Skewness2.4378084
Sum9.8958681 × 1012
Variance5.3061067 × 1014
MonotonicityNot monotonic
2025-07-07T15:02:01.623496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
258184 32401
 
4.5%
266293 26794
 
3.7%
88352 26447
 
3.7%
253718 20677
 
2.9%
24126601 20555
 
2.9%
20826740 20473
 
2.9%
394556 20321
 
2.8%
12996058 18392
 
2.6%
13068008 15920
 
2.2%
35639601 14555
 
2.0%
Other values (1207) 499496
69.8%
ValueCountFrequency (%)
-5 3182
0.4%
20056 18
 
< 0.1%
24401 675
 
0.1%
24423 117
 
< 0.1%
24434 41
 
< 0.1%
24984 17
 
< 0.1%
25974 333
 
< 0.1%
27250 176
 
< 0.1%
27261 198
 
< 0.1%
27756 242
 
< 0.1%
ValueCountFrequency (%)
145364511 3
 
< 0.1%
144258694 45
< 0.1%
142785140 2
 
< 0.1%
141582888 36
 
< 0.1%
141467111 81
< 0.1%
141313561 36
 
< 0.1%
140789743 27
 
< 0.1%
140722211 8
 
< 0.1%
139769944 64
< 0.1%
139178931 98
< 0.1%

usertype
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.8 MiB
11
306433 
21
250732 
-1
158659 
13
 
207

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1432062
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
11 306433
42.8%
21 250732
35.0%
-1 158659
22.2%
13 207
 
< 0.1%

Length

2025-07-07T15:02:01.660829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:01.684550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11 306433
42.8%
21 250732
35.0%
1 158659
22.2%
13 207
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 1022464
71.4%
2 250732
 
17.5%
- 158659
 
11.1%
3 207
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1432062
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1022464
71.4%
2 250732
 
17.5%
- 158659
 
11.1%
3 207
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1432062
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1022464
71.4%
2 250732
 
17.5%
- 158659
 
11.1%
3 207
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1432062
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1022464
71.4%
2 250732
 
17.5%
- 158659
 
11.1%
3 207
 
< 0.1%

submission_id
Real number (ℝ)

High correlation  Missing 

Distinct18261
Distinct (%)20.3%
Missing625966
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean36597646
Minimum1802
Maximum59929784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-07-07T15:02:01.716820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1802
5-th percentile24831
Q133464305
median43924991
Q350268921
95-th percentile58297382
Maximum59929784
Range59927982
Interquartile range (IQR)16804616

Descriptive statistics

Standard deviation20003069
Coefficient of variation (CV)0.54656711
Kurtosis-0.44142466
Mean36597646
Median Absolute Deviation (MAD)8395999
Skewness-1.0244067
Sum3.296167 × 1012
Variance4.0012279 × 1014
MonotonicityNot monotonic
2025-07-07T15:02:01.757505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53464285 9
 
< 0.1%
53464298 9
 
< 0.1%
53464400 9
 
< 0.1%
53464427 9
 
< 0.1%
53464519 9
 
< 0.1%
53604166 9
 
< 0.1%
53702374 9
 
< 0.1%
53828075 9
 
< 0.1%
53833386 9
 
< 0.1%
53933074 9
 
< 0.1%
Other values (18251) 89975
 
12.6%
(Missing) 625966
87.4%
ValueCountFrequency (%)
1802 3
 
< 0.1%
1803 3
 
< 0.1%
1866 9
< 0.1%
2158 9
< 0.1%
2159 9
< 0.1%
2529 9
< 0.1%
4434 9
< 0.1%
4439 9
< 0.1%
4440 9
< 0.1%
5909 9
< 0.1%
ValueCountFrequency (%)
59929784 9
< 0.1%
59928075 9
< 0.1%
59928054 9
< 0.1%
59928005 9
< 0.1%
59927104 1
 
< 0.1%
59927102 5
< 0.1%
59927101 1
 
< 0.1%
59927100 9
< 0.1%
59927098 1
 
< 0.1%
59927096 1
 
< 0.1%
Distinct162932
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
Minimum2012-07-03 13:55:51.727148
Maximum2025-03-27 21:04:36.624532
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T15:02:01.796200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:02:01.841829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct162932
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Memory size5.5 MiB
Minimum2012-07-03 13:55:51.727132
Maximum2025-03-27 21:04:36.624532
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T15:02:01.887347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:02:01.935176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

sts_id
Real number (ℝ)

High correlation 

Distinct716029
Distinct (%)100.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean361835.69
Minimum1
Maximum729308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-07-07T15:02:01.983079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35802.4
Q1179108
median360829
Q3542130
95-th percentile692389.6
Maximum729308
Range729307
Interquartile range (IQR)363022

Descriptive statistics

Standard deviation210434.79
Coefficient of variation (CV)0.58157555
Kurtosis-1.196188
Mean361835.69
Median Absolute Deviation (MAD)181511
Skewness0.016931274
Sum2.5908485 × 1011
Variance4.42828 × 1010
MonotonicityNot monotonic
2025-07-07T15:02:02.026771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
363556 1
 
< 0.1%
363548 1
 
< 0.1%
363549 1
 
< 0.1%
363550 1
 
< 0.1%
363551 1
 
< 0.1%
363552 1
 
< 0.1%
363553 1
 
< 0.1%
363554 1
 
< 0.1%
363555 1
 
< 0.1%
363557 1
 
< 0.1%
Other values (716019) 716019
> 99.9%
(Missing) 2
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
729308 1
< 0.1%
729307 1
< 0.1%
729306 1
< 0.1%
729305 1
< 0.1%
729304 1
< 0.1%
729303 1
< 0.1%
729302 1
< 0.1%
729301 1
< 0.1%
729300 1
< 0.1%
729299 1
< 0.1%

sample_number
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.3511101
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-07-07T15:02:02.059188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7292103
Coefficient of variation (CV)0.6272446
Kurtosis-1.2779063
Mean4.3511101
Median Absolute Deviation (MAD)2
Skewness0.25927688
Sum3115521
Variance7.4485888
MonotonicityNot monotonic
2025-07-07T15:02:02.086769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 162930
22.8%
2 79099
11.0%
3 77363
10.8%
4 68830
9.6%
5 67441
9.4%
6 65947
9.2%
7 65451
9.1%
8 64772
 
9.0%
9 64196
 
9.0%
(Missing) 2
 
< 0.1%
ValueCountFrequency (%)
1 162930
22.8%
2 79099
11.0%
3 77363
10.8%
4 68830
9.6%
5 67441
9.4%
6 65947
9.2%
7 65451
9.1%
8 64772
 
9.0%
9 64196
 
9.0%
ValueCountFrequency (%)
9 64196
 
9.0%
8 64772
 
9.0%
7 65451
9.1%
6 65947
9.2%
5 67441
9.4%
4 68830
9.6%
3 77363
10.8%
2 79099
11.0%
1 162930
22.8%

sample_surface_temperature_c
Real number (ℝ)

High correlation 

Distinct2637
Distinct (%)0.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20.270503
Minimum-56
Maximum72.5
Zeros3376
Zeros (%)0.5%
Negative68595
Negative (%)9.6%
Memory size5.5 MiB
2025-07-07T15:02:02.122444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-56
5-th percentile-3.2
Q17
median19.8
Q332.3
95-th percentile47.5
Maximum72.5
Range128.5
Interquartile range (IQR)25.3

Descriptive statistics

Standard deviation16.248506
Coefficient of variation (CV)0.80158377
Kurtosis-0.44873669
Mean20.270503
Median Absolute Deviation (MAD)12.6
Skewness0.24713939
Sum14514268
Variance264.01394
MonotonicityNot monotonic
2025-07-07T15:02:02.162781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 7646
 
1.1%
34 5048
 
0.7%
30 4311
 
0.6%
5 3898
 
0.5%
32 3882
 
0.5%
29 3856
 
0.5%
35 3793
 
0.5%
31 3790
 
0.5%
4 3700
 
0.5%
33 3681
 
0.5%
Other values (2627) 672424
93.9%
ValueCountFrequency (%)
-56 2
< 0.1%
-55.2 1
< 0.1%
-50.3 1
< 0.1%
-49.5 1
< 0.1%
-48.9 1
< 0.1%
-48.4 1
< 0.1%
-45.3 1
< 0.1%
-45 2
< 0.1%
-44.7 1
< 0.1%
-44.6 1
< 0.1%
ValueCountFrequency (%)
72.5 45
< 0.1%
72.4 35
< 0.1%
72.3 48
< 0.1%
72.2 33
< 0.1%
72.1 39
< 0.1%
72 71
< 0.1%
71.9 26
 
< 0.1%
71.8 27
 
< 0.1%
71.7 22
 
< 0.1%
71.6 26
 
< 0.1%

sample_snow_depth_mm
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct431
Distinct (%)0.3%
Missing547828
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean12.369543
Minimum0
Maximum1200
Zeros142620
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-07-07T15:02:02.202202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile82
Maximum1200
Range1200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation45.472407
Coefficient of variation (CV)3.6761591
Kurtosis88.337956
Mean12.369543
Median Absolute Deviation (MAD)0
Skewness7.1711611
Sum2080594.2
Variance2067.7398
MonotonicityNot monotonic
2025-07-07T15:02:02.245878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 142620
 
19.9%
20 1973
 
0.3%
10 1644
 
0.2%
30 1636
 
0.2%
40 1630
 
0.2%
50 1545
 
0.2%
60 1062
 
0.1%
100 793
 
0.1%
15 649
 
0.1%
70 647
 
0.1%
Other values (421) 14004
 
2.0%
(Missing) 547828
76.5%
ValueCountFrequency (%)
0 142620
19.9%
1 1
 
< 0.1%
2 1
 
< 0.1%
10 1644
 
0.2%
10.3 1
 
< 0.1%
10.5 2
 
< 0.1%
11 202
 
< 0.1%
11.3 3
 
< 0.1%
11.4 1
 
< 0.1%
11.5 12
 
< 0.1%
ValueCountFrequency (%)
1200 9
< 0.1%
1100 3
 
< 0.1%
1000 4
 
< 0.1%
900 21
< 0.1%
850 1
 
< 0.1%
800 4
 
< 0.1%
750 4
 
< 0.1%
740 1
 
< 0.1%
710 1
 
< 0.1%
700 8
 
< 0.1%

sample_snow_depth_flag
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing691406
Missing (%)96.6%
Memory size38.2 MiB
trace
14757 
measurable
7258 
zero
2610 

Length

Max length10
Median length5
Mean length6.3677157
Min length4

Characters and Unicode

Total characters156805
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtrace
2nd rowtrace
3rd rowtrace
4th rowtrace
5th rowtrace

Common Values

ValueCountFrequency (%)
trace 14757
 
2.1%
measurable 7258
 
1.0%
zero 2610
 
0.4%
(Missing) 691406
96.6%

Length

2025-07-07T15:02:02.285078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:02.307400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
trace 14757
59.9%
measurable 7258
29.5%
zero 2610
 
10.6%

Most occurring characters

ValueCountFrequency (%)
e 31883
20.3%
a 29273
18.7%
r 24625
15.7%
t 14757
9.4%
c 14757
9.4%
m 7258
 
4.6%
s 7258
 
4.6%
u 7258
 
4.6%
b 7258
 
4.6%
l 7258
 
4.6%
Other values (2) 5220
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 156805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 31883
20.3%
a 29273
18.7%
r 24625
15.7%
t 14757
9.4%
c 14757
9.4%
m 7258
 
4.6%
s 7258
 
4.6%
u 7258
 
4.6%
b 7258
 
4.6%
l 7258
 
4.6%
Other values (2) 5220
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 156805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 31883
20.3%
a 29273
18.7%
r 24625
15.7%
t 14757
9.4%
c 14757
9.4%
m 7258
 
4.6%
s 7258
 
4.6%
u 7258
 
4.6%
b 7258
 
4.6%
l 7258
 
4.6%
Other values (2) 5220
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 156805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 31883
20.3%
a 29273
18.7%
r 24625
15.7%
t 14757
9.4%
c 14757
9.4%
m 7258
 
4.6%
s 7258
 
4.6%
u 7258
 
4.6%
b 7258
 
4.6%
l 7258
 
4.6%
Other values (2) 5220
 
3.3%

version_id
Real number (ℝ)

High correlation  Missing 

Distinct6178
Distinct (%)1.0%
Missing100398
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean14573.791
Minimum4356
Maximum105432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-07-07T15:02:02.341158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4356
5-th percentile7484
Q18353
median10916
Q313040
95-th percentile45363
Maximum105432
Range101076
Interquartile range (IQR)4687

Descriptive statistics

Standard deviation14297.815
Coefficient of variation (CV)0.98106357
Kurtosis13.317196
Mean14573.791
Median Absolute Deviation (MAD)2464
Skewness3.5949941
Sum8.9721065 × 109
Variance2.0442752 × 108
MonotonicityNot monotonic
2025-07-07T15:02:02.382711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11421 20555
 
2.9%
13040 18347
 
2.6%
11320 13088
 
1.8%
11596 12169
 
1.7%
8137 9250
 
1.3%
11747 8164
 
1.1%
10418 7816
 
1.1%
11456 7414
 
1.0%
11241 5613
 
0.8%
11457 5433
 
0.8%
Other values (6168) 507784
70.9%
(Missing) 100398
 
14.0%
ValueCountFrequency (%)
4356 336
 
< 0.1%
4361 3
 
< 0.1%
4362 3
 
< 0.1%
4365 18
 
< 0.1%
4366 126
 
< 0.1%
4367 108
 
< 0.1%
4369 81
 
< 0.1%
4370 411
 
0.1%
4372 3691
0.5%
4373 1441
 
0.2%
ValueCountFrequency (%)
105432 1
 
< 0.1%
105431 1
 
< 0.1%
105422 1
 
< 0.1%
105400 28
< 0.1%
105383 9
 
< 0.1%
105380 39
< 0.1%
105366 1
 
< 0.1%
105363 1
 
< 0.1%
105362 1
 
< 0.1%
105358 1
 
< 0.1%

version
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct525
Distinct (%)0.1%
Missing100398
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean8.4001913
Minimum1
Maximum3541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2025-07-07T15:02:02.422639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile14
Maximum3541
Range3540
Interquartile range (IQR)3

Descriptive statistics

Standard deviation63.032819
Coefficient of variation (CV)7.5037361
Kurtosis1070.3421
Mean8.4001913
Median Absolute Deviation (MAD)1
Skewness26.948897
Sum5171435
Variance3973.1363
MonotonicityNot monotonic
2025-07-07T15:02:02.463757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 288586
40.3%
2 98958
 
13.8%
3 54146
 
7.6%
4 46268
 
6.5%
5 25541
 
3.6%
7 25270
 
3.5%
8 18176
 
2.5%
6 8230
 
1.1%
9 4694
 
0.7%
11 4194
 
0.6%
Other values (515) 41570
 
5.8%
(Missing) 100398
 
14.0%
ValueCountFrequency (%)
1 288586
40.3%
2 98958
 
13.8%
3 54146
 
7.6%
4 46268
 
6.5%
5 25541
 
3.6%
6 8230
 
1.1%
7 25270
 
3.5%
8 18176
 
2.5%
9 4694
 
0.7%
10 3114
 
0.4%
ValueCountFrequency (%)
3541 18
 
< 0.1%
3336 18
 
< 0.1%
3272 9
 
< 0.1%
3202 9
 
< 0.1%
3132 18
 
< 0.1%
1545 18
 
< 0.1%
1512 18
 
< 0.1%
1482 54
< 0.1%
1480 9
 
< 0.1%
1463 18
 
< 0.1%
Distinct5312
Distinct (%)0.9%
Missing100398
Missing (%)14.0%
Memory size5.5 MiB
Minimum1999-04-13 16:12:00
Maximum2025-03-27 06:51:06.819409
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T15:02:02.590090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:02:02.631007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

version_date
Date

Missing 

Distinct6083
Distinct (%)1.0%
Missing100398
Missing (%)14.0%
Memory size5.5 MiB
Minimum2003-08-27 19:05:39
Maximum2025-03-27 06:51:06.819399
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T15:02:02.669322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:02:02.711021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

site_version_comments
Text

Missing 

Distinct450
Distinct (%)0.1%
Missing369536
Missing (%)51.6%
Memory size55.2 MiB
2025-07-07T15:02:02.903704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length622
Median length53
Mean length50.706576
Min length1

Characters and Unicode

Total characters17569575
Distinct characters191
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowplease replace with Surface Temperature Site Comments
2nd rowplease replace with Surface Temperature Site Comments
3rd rowplease replace with Surface Temperature Site Comments
4th rowplease replace with Surface Temperature Site Comments
5th rowplease replace with Surface Temperature Site Comments
ValueCountFrequency (%)
site 206054
 
7.8%
please 194303
 
7.3%
surface 187321
 
7.1%
temperature 184778
 
7.0%
with 181127
 
6.8%
comments 176096
 
6.7%
replace 175899
 
6.6%
the 60952
 
2.3%
of 43986
 
1.7%
is 33940
 
1.3%
Other values (1257) 1201320
45.4%
2025-07-07T15:02:03.147686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2527427
14.4%
2326736
13.2%
t 1229693
 
7.0%
a 1222991
 
7.0%
r 1071981
 
6.1%
i 750739
 
4.3%
m 719286
 
4.1%
s 709174
 
4.0%
p 708644
 
4.0%
o 628468
 
3.6%
Other values (181) 5674436
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17569575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2527427
14.4%
2326736
13.2%
t 1229693
 
7.0%
a 1222991
 
7.0%
r 1071981
 
6.1%
i 750739
 
4.3%
m 719286
 
4.1%
s 709174
 
4.0%
p 708644
 
4.0%
o 628468
 
3.6%
Other values (181) 5674436
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17569575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2527427
14.4%
2326736
13.2%
t 1229693
 
7.0%
a 1222991
 
7.0%
r 1071981
 
6.1%
i 750739
 
4.3%
m 719286
 
4.1%
s 709174
 
4.0%
p 708644
 
4.0%
o 628468
 
3.6%
Other values (181) 5674436
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17569575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2527427
14.4%
2326736
13.2%
t 1229693
 
7.0%
a 1222991
 
7.0%
r 1071981
 
6.1%
i 750739
 
4.3%
m 719286
 
4.1%
s 709174
 
4.0%
p 708644
 
4.0%
o 628468
 
3.6%
Other values (181) 5674436
32.3%

homogeneous_site_short_length_m
Real number (ℝ)

High correlation  Missing 

Distinct102
Distinct (%)< 0.1%
Missing153800
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean29.538096
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-07-07T15:02:03.192952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.25
Q110
median30
Q330
95-th percentile90
Maximum100
Range99
Interquartile range (IQR)20

Descriptive statistics

Standard deviation24.913682
Coefficient of variation (CV)0.84344238
Kurtosis1.4530351
Mean29.538096
Median Absolute Deviation (MAD)9
Skewness1.4366471
Sum16607233
Variance620.69153
MonotonicityNot monotonic
2025-07-07T15:02:03.233566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 261624
36.5%
10 69831
 
9.8%
90 61303
 
8.6%
1 22262
 
3.1%
5 20395
 
2.8%
2.25 18302
 
2.6%
3 16989
 
2.4%
15 10997
 
1.5%
20 9983
 
1.4%
50 9031
 
1.3%
Other values (92) 61514
 
8.6%
(Missing) 153800
21.5%
ValueCountFrequency (%)
1 22262
3.1%
1.5 170
 
< 0.1%
1.9 171
 
< 0.1%
2 932
 
0.1%
2.25 18302
2.6%
2.4 51
 
< 0.1%
2.5 225
 
< 0.1%
3 16989
2.4%
3.5 9
 
< 0.1%
3.61 9
 
< 0.1%
ValueCountFrequency (%)
100 3240
 
0.5%
97 1
 
< 0.1%
95 9
 
< 0.1%
92.35 52
 
< 0.1%
90 61303
8.6%
83.83 9
 
< 0.1%
80 115
 
< 0.1%
78.98 27
 
< 0.1%
78.6 6
 
< 0.1%
70 173
 
< 0.1%

homogeneous_site_long_length_m
Real number (ℝ)

High correlation  Missing 

Distinct109
Distinct (%)< 0.1%
Missing153819
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean32.712444
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 MiB
2025-07-07T15:02:03.273241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.25
Q115
median30
Q330
95-th percentile90
Maximum100
Range99
Interquartile range (IQR)15

Descriptive statistics

Standard deviation25.456198
Coefficient of variation (CV)0.77818087
Kurtosis0.7582972
Mean32.712444
Median Absolute Deviation (MAD)6
Skewness1.2215133
Sum18391328
Variance648.01802
MonotonicityNot monotonic
2025-07-07T15:02:03.312532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 268174
37.5%
90 67482
 
9.4%
10 55769
 
7.8%
1 18388
 
2.6%
2.25 18302
 
2.6%
3 17912
 
2.5%
5 15945
 
2.2%
40 13206
 
1.8%
15 11475
 
1.6%
20 8112
 
1.1%
Other values (99) 67447
 
9.4%
(Missing) 153819
21.5%
ValueCountFrequency (%)
1 18388
2.6%
1.45 36
 
< 0.1%
1.5 179
 
< 0.1%
2 146
 
< 0.1%
2.25 18302
2.6%
2.286 377
 
0.1%
2.4 24
 
< 0.1%
3 17912
2.5%
3.33 36
 
< 0.1%
3.6 27
 
< 0.1%
ValueCountFrequency (%)
100 2697
 
0.4%
97 1
 
< 0.1%
90 67482
9.4%
80.1 15
 
< 0.1%
78.83 9
 
< 0.1%
78.6 6
 
< 0.1%
78 1006
 
0.1%
75 4158
 
0.6%
71 45
 
< 0.1%
70 137
 
< 0.1%

surface_cover_type
Categorical

High correlation  Imbalance  Missing 

Distinct21
Distinct (%)< 0.1%
Missing163529
Missing (%)22.8%
Memory size39.6 MiB
short grass
335803 
asphalt
92202 
other
53345 
concrete
35749 
tall grass
 
10842
Other values (16)
 
24561

Length

Max length16
Median length11
Mean length9.5359619
Min length4

Characters and Unicode

Total characters5268638
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowshort grass
2nd rowshort grass
3rd rowshort grass
4th rowshort grass
5th rowshort grass

Common Values

ValueCountFrequency (%)
short grass 335803
46.9%
asphalt 92202
 
12.9%
other 53345
 
7.5%
concrete 35749
 
5.0%
tall grass 10842
 
1.5%
dry bare ground 8823
 
1.2%
not provided 3365
 
0.5%
open water 3134
 
0.4%
sand 2295
 
0.3%
bare 1712
 
0.2%
Other values (11) 5232
 
0.7%
(Missing) 163529
22.8%

Length

2025-07-07T15:02:03.352705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
grass 346645
37.5%
short 335803
36.3%
asphalt 92202
 
10.0%
other 53345
 
5.8%
concrete 35749
 
3.9%
tall 10842
 
1.2%
bare 10535
 
1.1%
dry 8823
 
1.0%
ground 8823
 
1.0%
not 3365
 
0.4%
Other values (19) 19342
 
2.1%

Most occurring characters

ValueCountFrequency (%)
s 1127223
21.4%
r 812048
15.4%
a 562454
10.7%
t 538028
10.2%
h 483099
9.2%
o 443965
 
8.4%
372972
 
7.1%
g 355468
 
6.7%
e 147181
 
2.8%
l 116380
 
2.2%
Other values (13) 309820
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5268638
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 1127223
21.4%
r 812048
15.4%
a 562454
10.7%
t 538028
10.2%
h 483099
9.2%
o 443965
 
8.4%
372972
 
7.1%
g 355468
 
6.7%
e 147181
 
2.8%
l 116380
 
2.2%
Other values (13) 309820
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5268638
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 1127223
21.4%
r 812048
15.4%
a 562454
10.7%
t 538028
10.2%
h 483099
9.2%
o 443965
 
8.4%
372972
 
7.1%
g 355468
 
6.7%
e 147181
 
2.8%
l 116380
 
2.2%
Other values (13) 309820
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5268638
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 1127223
21.4%
r 812048
15.4%
a 562454
10.7%
t 538028
10.2%
h 483099
9.2%
o 443965
 
8.4%
372972
 
7.1%
g 355468
 
6.7%
e 147181
 
2.8%
l 116380
 
2.2%
Other values (13) 309820
 
5.9%

instrument_type
Text

Missing 

Distinct393
Distinct (%)0.1%
Missing245047
Missing (%)34.2%
Memory size41.4 MiB
2025-07-07T15:02:03.503022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length90
Median length82
Mean length13.202253
Min length1

Characters and Unicode

Total characters6218050
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowraytek st20
2nd rowraytek st20
3rd rowraytek st20
4th rowraytek st20
5th rowraytek st20
ValueCountFrequency (%)
st20 210349
20.6%
raytek 131723
 
12.9%
raytech 84712
 
8.3%
fluke 61493
 
6.0%
63 41567
 
4.1%
sper 37211
 
3.6%
scientific 33811
 
3.3%
800103 31981
 
3.1%
st650 25468
 
2.5%
xindar 18347
 
1.8%
Other values (339) 343741
33.7%
2025-07-07T15:02:03.699996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
570386
 
9.2%
e 520170
 
8.4%
t 474415
 
7.6%
0 453069
 
7.3%
a 323509
 
5.2%
r 306786
 
4.9%
2 286091
 
4.6%
y 228493
 
3.7%
T 226531
 
3.6%
k 201905
 
3.2%
Other values (72) 2626695
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6218050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
570386
 
9.2%
e 520170
 
8.4%
t 474415
 
7.6%
0 453069
 
7.3%
a 323509
 
5.2%
r 306786
 
4.9%
2 286091
 
4.6%
y 228493
 
3.7%
T 226531
 
3.6%
k 201905
 
3.2%
Other values (72) 2626695
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6218050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
570386
 
9.2%
e 520170
 
8.4%
t 474415
 
7.6%
0 453069
 
7.3%
a 323509
 
5.2%
r 306786
 
4.9%
2 286091
 
4.6%
y 228493
 
3.7%
T 226531
 
3.6%
k 201905
 
3.2%
Other values (72) 2626695
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6218050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
570386
 
9.2%
e 520170
 
8.4%
t 474415
 
7.6%
0 453069
 
7.3%
a 323509
 
5.2%
r 306786
 
4.9%
2 286091
 
4.6%
y 228493
 
3.7%
T 226531
 
3.6%
k 201905
 
3.2%
Other values (72) 2626695
42.2%

protocol_name
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.4 MiB
Surface Temperature
716031 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters13604589
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSurface Temperature
2nd rowSurface Temperature
3rd rowSurface Temperature
4th rowSurface Temperature
5th rowSurface Temperature

Common Values

ValueCountFrequency (%)
Surface Temperature 716031
100.0%

Length

2025-07-07T15:02:03.742117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:03.761783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surface 716031
50.0%
temperature 716031
50.0%

Most occurring characters

ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
S 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
716031
 
5.3%
T 716031
 
5.3%
m 716031
 
5.3%
Other values (2) 1432062
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13604589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
S 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
716031
 
5.3%
T 716031
 
5.3%
m 716031
 
5.3%
Other values (2) 1432062
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13604589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
S 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
716031
 
5.3%
T 716031
 
5.3%
m 716031
 
5.3%
Other values (2) 1432062
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13604589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
S 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
716031
 
5.3%
T 716031
 
5.3%
m 716031
 
5.3%
Other values (2) 1432062
10.5%

protocol_model
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.8 MiB
SurfaceTemperature
716031 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters12888558
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSurfaceTemperature
2nd rowSurfaceTemperature
3rd rowSurfaceTemperature
4th rowSurfaceTemperature
5th rowSurfaceTemperature

Common Values

ValueCountFrequency (%)
SurfaceTemperature 716031
100.0%

Length

2025-07-07T15:02:03.785895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:03.805705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surfacetemperature 716031
100.0%

Most occurring characters

ValueCountFrequency (%)
e 2864124
22.2%
r 2148093
16.7%
u 1432062
11.1%
a 1432062
11.1%
S 716031
 
5.6%
f 716031
 
5.6%
c 716031
 
5.6%
T 716031
 
5.6%
m 716031
 
5.6%
p 716031
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12888558
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2864124
22.2%
r 2148093
16.7%
u 1432062
11.1%
a 1432062
11.1%
S 716031
 
5.6%
f 716031
 
5.6%
c 716031
 
5.6%
T 716031
 
5.6%
m 716031
 
5.6%
p 716031
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12888558
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2864124
22.2%
r 2148093
16.7%
u 1432062
11.1%
a 1432062
11.1%
S 716031
 
5.6%
f 716031
 
5.6%
c 716031
 
5.6%
T 716031
 
5.6%
m 716031
 
5.6%
p 716031
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12888558
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2864124
22.2%
r 2148093
16.7%
u 1432062
11.1%
a 1432062
11.1%
S 716031
 
5.6%
f 716031
 
5.6%
c 716031
 
5.6%
T 716031
 
5.6%
m 716031
 
5.6%
p 716031
 
5.6%

protocol_association_name
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.4 MiB
surface_temperature
716031 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters13604589
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsurface_temperature
2nd rowsurface_temperature
3rd rowsurface_temperature
4th rowsurface_temperature
5th rowsurface_temperature

Common Values

ValueCountFrequency (%)
surface_temperature 716031
100.0%

Length

2025-07-07T15:02:03.829165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:03.848123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surface_temperature 716031
100.0%

Most occurring characters

ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
t 1432062
10.5%
s 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
_ 716031
 
5.3%
m 716031
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13604589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
t 1432062
10.5%
s 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
_ 716031
 
5.3%
m 716031
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13604589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
t 1432062
10.5%
s 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
_ 716031
 
5.3%
m 716031
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13604589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
t 1432062
10.5%
s 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
_ 716031
 
5.3%
m 716031
 
5.3%

protocol_alt_name
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.4 MiB
Surface Temperature
716031 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters13604589
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSurface Temperature
2nd rowSurface Temperature
3rd rowSurface Temperature
4th rowSurface Temperature
5th rowSurface Temperature

Common Values

ValueCountFrequency (%)
Surface Temperature 716031
100.0%

Length

2025-07-07T15:02:03.872459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:03.891317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surface 716031
50.0%
temperature 716031
50.0%

Most occurring characters

ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
S 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
716031
 
5.3%
T 716031
 
5.3%
m 716031
 
5.3%
Other values (2) 1432062
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13604589
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
S 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
716031
 
5.3%
T 716031
 
5.3%
m 716031
 
5.3%
Other values (2) 1432062
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13604589
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
S 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
716031
 
5.3%
T 716031
 
5.3%
m 716031
 
5.3%
Other values (2) 1432062
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13604589
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2864124
21.1%
r 2148093
15.8%
u 1432062
10.5%
a 1432062
10.5%
S 716031
 
5.3%
f 716031
 
5.3%
c 716031
 
5.3%
716031
 
5.3%
T 716031
 
5.3%
m 716031
 
5.3%
Other values (2) 1432062
10.5%

protocol_investigation_area
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.3 MiB
Atmosphere
716031 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7160310
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAtmosphere
2nd rowAtmosphere
3rd rowAtmosphere
4th rowAtmosphere
5th rowAtmosphere

Common Values

ValueCountFrequency (%)
Atmosphere 716031
100.0%

Length

2025-07-07T15:02:03.914715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:03.934306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
atmosphere 716031
100.0%

Most occurring characters

ValueCountFrequency (%)
e 1432062
20.0%
A 716031
10.0%
t 716031
10.0%
m 716031
10.0%
o 716031
10.0%
s 716031
10.0%
p 716031
10.0%
h 716031
10.0%
r 716031
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7160310
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1432062
20.0%
A 716031
10.0%
t 716031
10.0%
m 716031
10.0%
o 716031
10.0%
s 716031
10.0%
p 716031
10.0%
h 716031
10.0%
r 716031
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7160310
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1432062
20.0%
A 716031
10.0%
t 716031
10.0%
m 716031
10.0%
o 716031
10.0%
s 716031
10.0%
p 716031
10.0%
h 716031
10.0%
r 716031
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7160310
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1432062
20.0%
A 716031
10.0%
t 716031
10.0%
m 716031
10.0%
o 716031
10.0%
s 716031
10.0%
p 716031
10.0%
h 716031
10.0%
r 716031
10.0%

user_type_description
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.6 MiB
non-student user - trained
306433 
student user - trained
250732 
not categorized
158659 
non-student user - untrained
 
207

Length

Max length28
Median length26
Mean length22.162508
Min length15

Characters and Unicode

Total characters15869043
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot categorized
2nd rownot categorized
3rd rownot categorized
4th rownot categorized
5th rownot categorized

Common Values

ValueCountFrequency (%)
non-student user - trained 306433
42.8%
student user - trained 250732
35.0%
not categorized 158659
22.2%
non-student user - untrained 207
 
< 0.1%

Length

2025-07-07T15:02:03.958370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:03.982292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
user 557372
21.9%
557372
21.9%
trained 557165
21.9%
non-student 306640
12.0%
student 250732
9.8%
not 158659
 
6.2%
categorized 158659
 
6.2%
untrained 207
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 1989434
12.5%
e 1989434
12.5%
n 1886890
11.9%
1830775
11.5%
d 1273403
8.0%
r 1273403
8.0%
u 1114951
7.0%
s 1114744
7.0%
- 864012
5.4%
a 716031
 
4.5%
Other values (5) 1815966
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15869043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1989434
12.5%
e 1989434
12.5%
n 1886890
11.9%
1830775
11.5%
d 1273403
8.0%
r 1273403
8.0%
u 1114951
7.0%
s 1114744
7.0%
- 864012
5.4%
a 716031
 
4.5%
Other values (5) 1815966
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15869043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1989434
12.5%
e 1989434
12.5%
n 1886890
11.9%
1830775
11.5%
d 1273403
8.0%
r 1273403
8.0%
u 1114951
7.0%
s 1114744
7.0%
- 864012
5.4%
a 716031
 
4.5%
Other values (5) 1815966
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15869043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1989434
12.5%
e 1989434
12.5%
n 1886890
11.9%
1830775
11.5%
d 1273403
8.0%
r 1273403
8.0%
u 1114951
7.0%
s 1114744
7.0%
- 864012
5.4%
a 716031
 
4.5%
Other values (5) 1815966
11.4%

submission_comments
Text

Missing 

Distinct1003
Distinct (%)10.3%
Missing706334
Missing (%)98.6%
Memory size38.5 MiB
2025-07-07T15:02:04.164925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length365
Median length139
Mean length28.737754
Min length1

Characters and Unicode

Total characters278670
Distinct characters137
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)1.8%

Sample

1st rowGrassy area varies in height and coverage. dirt patches and taller grass in the Northeast corner of site.
2nd rowGrassy area varies in height and coverage. dirt patches and taller grass in the Northeast corner of site.
3rd rowGrassy area varies in height and coverage. dirt patches and taller grass in the Northeast corner of site.
4th rowGrassy area varies in height and coverage. dirt patches and taller grass in the Northeast corner of site.
5th rowGrassy area varies in height and coverage. dirt patches and taller grass in the Northeast corner of site.
ValueCountFrequency (%)
not 1518
 
3.0%
data 1506
 
3.0%
do 1436
 
2.9%
delete 1427
 
2.8%
scrc 1427
 
2.8%
please 1427
 
2.8%
the 1408
 
2.8%
me 1391
 
2.8%
asphalt 1144
 
2.3%
grass 918
 
1.8%
Other values (1172) 36491
72.8%
2025-07-07T15:02:04.405866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40421
14.5%
e 26528
 
9.5%
a 18725
 
6.7%
t 16543
 
5.9%
s 15192
 
5.5%
o 14721
 
5.3%
d 13673
 
4.9%
l 13322
 
4.8%
n 12727
 
4.6%
r 12403
 
4.5%
Other values (127) 94415
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 278670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
40421
14.5%
e 26528
 
9.5%
a 18725
 
6.7%
t 16543
 
5.9%
s 15192
 
5.5%
o 14721
 
5.3%
d 13673
 
4.9%
l 13322
 
4.8%
n 12727
 
4.6%
r 12403
 
4.5%
Other values (127) 94415
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 278670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
40421
14.5%
e 26528
 
9.5%
a 18725
 
6.7%
t 16543
 
5.9%
s 15192
 
5.5%
o 14721
 
5.3%
d 13673
 
4.9%
l 13322
 
4.8%
n 12727
 
4.6%
r 12403
 
4.5%
Other values (127) 94415
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 278670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
40421
14.5%
e 26528
 
9.5%
a 18725
 
6.7%
t 16543
 
5.9%
s 15192
 
5.5%
o 14721
 
5.3%
d 13673
 
4.9%
l 13322
 
4.8%
n 12727
 
4.6%
r 12403
 
4.5%
Other values (127) 94415
33.9%

submission_developer_key_id
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing625971
Missing (%)87.4%
Memory size37.7 MiB
5
71165 
1
18895 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters90060
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
5 71165
 
9.9%
1 18895
 
2.6%
(Missing) 625971
87.4%

Length

2025-07-07T15:02:04.446665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:04.464913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 71165
79.0%
1 18895
 
21.0%

Most occurring characters

ValueCountFrequency (%)
5 71165
79.0%
1 18895
 
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 71165
79.0%
1 18895
 
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 71165
79.0%
1 18895
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 71165
79.0%
1 18895
 
21.0%

submission_access_code_id
Unsupported

Missing  Rejected  Unsupported 

Missing716031
Missing (%)100.0%
Memory size6.1 MiB

submission_latitude
Real number (ℝ)

High correlation  Missing 

Distinct1565
Distinct (%)2.2%
Missing644866
Missing (%)90.1%
Infinite0
Infinite (%)0.0%
Mean31.524425
Minimum-39.958297
Maximum89.999997
Zeros3
Zeros (%)< 0.1%
Negative4452
Negative (%)0.6%
Memory size5.5 MiB
2025-07-07T15:02:04.493505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-39.958297
5-th percentile-7.2363
Q126.601844
median35.9087
Q341.56102
95-th percentile42.82463
Maximum89.999997
Range129.95829
Interquartile range (IQR)14.959176

Descriptive statistics

Standard deviation16.876146
Coefficient of variation (CV)0.53533556
Kurtosis6.0426013
Mean31.524425
Median Absolute Deviation (MAD)5.7068
Skewness-1.614503
Sum2243435.7
Variance284.80429
MonotonicityNot monotonic
2025-07-07T15:02:04.534641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.9752 4485
 
0.6%
36.290455 4380
 
0.6%
36.323437 3483
 
0.5%
-7.2363 1956
 
0.3%
26.601844 1914
 
0.3%
24.681796 1740
 
0.2%
36.25997 1089
 
0.2%
35.8879 1001
 
0.1%
40.096769 909
 
0.1%
89.999997 692
 
0.1%
Other values (1555) 49516
 
6.9%
(Missing) 644866
90.1%
ValueCountFrequency (%)
-39.958297 7
 
< 0.1%
-39.957271 6
 
< 0.1%
-39.95725 20
 
< 0.1%
-39.957043 14
 
< 0.1%
-39.95311 14
 
< 0.1%
-39.952469 63
< 0.1%
-39.951394 18
 
< 0.1%
-38.94135 27
 
< 0.1%
-38.94134 54
< 0.1%
-38.94098 74
< 0.1%
ValueCountFrequency (%)
89.999997 692
0.1%
85 27
 
< 0.1%
68.40352 3
 
< 0.1%
68.36137 1
 
< 0.1%
68.32821 1
 
< 0.1%
68.29618 1
 
< 0.1%
64.87418 3
 
< 0.1%
64.86539 3
 
< 0.1%
64.85875 1
 
< 0.1%
64.844514 36
 
< 0.1%

submission_longitude
Real number (ℝ)

High correlation  Missing 

Distinct1599
Distinct (%)2.2%
Missing644866
Missing (%)90.1%
Infinite0
Infinite (%)0.0%
Mean-25.816088
Minimum-161.011
Maximum174
Zeros3
Zeros (%)< 0.1%
Negative42006
Negative (%)5.9%
Memory size5.5 MiB
2025-07-07T15:02:04.572988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-161.011
5-th percentile-93.296115
Q1-83.62744
median-71.57822
Q334.9299
95-th percentile121.5327
Maximum174
Range335.011
Interquartile range (IQR)118.55734

Descriptive statistics

Standard deviation74.57072
Coefficient of variation (CV)-2.8885368
Kurtosis-0.80314424
Mean-25.816088
Median Absolute Deviation (MAD)21.717895
Skewness0.66507516
Sum-1837201.9
Variance5560.7923
MonotonicityNot monotonic
2025-07-07T15:02:04.611299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121.5327 4486
 
0.6%
-93.296115 4380
 
0.6%
-93.258022 3483
 
0.5%
-39.4149 1956
 
0.3%
-81.94282 1914
 
0.3%
121.762254 1740
 
0.2%
-93.13447 1089
 
0.2%
14.4725 1001
 
0.1%
22.497501 909
 
0.1%
-83.584746 627
 
0.1%
Other values (1589) 49580
 
6.9%
(Missing) 644866
90.1%
ValueCountFrequency (%)
-161.011 330
< 0.1%
-160.561 207
< 0.1%
-160.401 155
< 0.1%
-149.41118 81
 
< 0.1%
-147.85322 1
 
< 0.1%
-147.724851 36
 
< 0.1%
-147.19618 36
 
< 0.1%
-141.74 3
 
< 0.1%
-141.7387 3
 
< 0.1%
-122.2757 1
 
< 0.1%
ValueCountFrequency (%)
174 27
 
< 0.1%
145.14711 27
 
< 0.1%
127.38938 45
 
< 0.1%
127.3288 1
 
< 0.1%
121.762254 1740
0.2%
121.76225 170
 
< 0.1%
121.76218 11
 
< 0.1%
121.762 108
 
< 0.1%
121.7306 3
 
< 0.1%
121.637597 1
 
< 0.1%

submission_elevation
Real number (ℝ)

High correlation  Missing 

Distinct1100
Distinct (%)1.5%
Missing644866
Missing (%)90.1%
Infinite0
Infinite (%)0.0%
Mean227.7479
Minimum-4228.8
Maximum7730
Zeros155
Zeros (%)< 0.1%
Negative409
Negative (%)0.1%
Memory size5.5 MiB
2025-07-07T15:02:04.649436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4228.8
5-th percentile5
Q153.1
median185.6
Q3361.9
95-th percentile497.2
Maximum7730
Range11958.8
Interquartile range (IQR)308.8

Descriptive statistics

Standard deviation296.30974
Coefficient of variation (CV)1.3010427
Kurtosis145.96631
Mean227.7479
Median Absolute Deviation (MAD)142.3
Skewness4.4534461
Sum16207679
Variance87799.464
MonotonicityNot monotonic
2025-07-07T15:02:04.689218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
378 4569
 
0.6%
30 4478
 
0.6%
454.2 3483
 
0.5%
5 1973
 
0.3%
497.2 1957
 
0.3%
51.2 1751
 
0.2%
400 1407
 
0.2%
192.5 1224
 
0.2%
361.9 1089
 
0.2%
43.3 1001
 
0.1%
Other values (1090) 48233
 
6.7%
(Missing) 644866
90.1%
ValueCountFrequency (%)
-4228.8 27
 
< 0.1%
-4011.6 9
 
< 0.1%
-3492 12
 
< 0.1%
-1040.8 12
 
< 0.1%
-393.2 70
< 0.1%
-34.4 126
< 0.1%
-31.2 14
 
< 0.1%
-13.3 9
 
< 0.1%
-4.9 1
 
< 0.1%
-4.5 2
 
< 0.1%
ValueCountFrequency (%)
7730 18
< 0.1%
3022.1 30
< 0.1%
3021.9 1
 
< 0.1%
3021.5 1
 
< 0.1%
2851 18
< 0.1%
2850.7 27
< 0.1%
2612 18
< 0.1%
2576.7 9
 
< 0.1%
2548.5 5
 
< 0.1%
2390.3 3
 
< 0.1%

submission_point
Text

Missing 

Distinct2037
Distinct (%)2.9%
Missing644866
Missing (%)90.1%
Memory size27.5 MiB
2025-07-07T15:02:04.946588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters4696890
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique483 ?
Unique (%)0.7%

Sample

1st row01010000A0E6100000CA54C1A8A4CA53408D28ED0DBE003A400000000000106340
2nd row01010000A0E6100000CA54C1A8A4CA53401B9E5E29CB003A403333333333F36240
3rd row01010000A0E6100000E605D847A7CA534029CB10C7BA003A409A99999999496340
4th row01010000A0E6100000DFE00B93A9CA534054E3A59BC4003A409A99999999396340
5th row01010000A0E6100000DFE00B93A9CA534054E3A59BC4003A409A99999999396340
ValueCountFrequency (%)
01010000a0e610000012a5bdc117625e40280f0bb5a6f938400000000000003e40 4476
 
6.3%
01010000a0e6100000b936548cf35257c0d7fa22a12d2542400000000000a07740 4380
 
6.2%
01010000a0e610000081e9b46e835057c07ea83462662942403333333333637c40 3483
 
4.9%
01010000a0e6100000228e75711bb543c0e02d90a0f8f11cc03333333333137f40 1956
 
2.7%
01010000a0e6100000f180b229577c54c0384bc972129a3a400000000000001440 1914
 
2.7%
01010000a0e6100000b14f00c5c8705e402a8bc22e8aae38409a99999999994940 1740
 
2.4%
01010000a0e6100000c2120f289b4857c077f86bb24621424066666666669e7640 1089
 
1.5%
01010000a0e610000052b81e85ebf12c40280f0bb5a6f141406666666666a64540 1001
 
1.4%
01010000a0e610000030babc395c7f3640212235ed620c44400000000000007940 909
 
1.3%
01010000a0e6100000e09d7c7a6ce554c0346612f582cf44406666666666b66740 627
 
0.9%
Other values (2027) 49590
69.7%
2025-07-07T15:02:05.236890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1523679
32.4%
4 385060
 
8.2%
1 366319
 
7.8%
6 324534
 
6.9%
3 297567
 
6.3%
C 254903
 
5.4%
A 207492
 
4.4%
E 190952
 
4.1%
9 186607
 
4.0%
5 170900
 
3.6%
Other values (6) 788877
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4696890
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1523679
32.4%
4 385060
 
8.2%
1 366319
 
7.8%
6 324534
 
6.9%
3 297567
 
6.3%
C 254903
 
5.4%
A 207492
 
4.4%
E 190952
 
4.1%
9 186607
 
4.0%
5 170900
 
3.6%
Other values (6) 788877
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4696890
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1523679
32.4%
4 385060
 
8.2%
1 366319
 
7.8%
6 324534
 
6.9%
3 297567
 
6.3%
C 254903
 
5.4%
A 207492
 
4.4%
E 190952
 
4.1%
9 186607
 
4.0%
5 170900
 
3.6%
Other values (6) 788877
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4696890
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1523679
32.4%
4 385060
 
8.2%
1 366319
 
7.8%
6 324534
 
6.9%
3 297567
 
6.3%
C 254903
 
5.4%
A 207492
 
4.4%
E 190952
 
4.1%
9 186607
 
4.0%
5 170900
 
3.6%
Other values (6) 788877
16.8%

submission_data
Text

Missing 

Distinct78
Distinct (%)0.3%
Missing693424
Missing (%)96.8%
Memory size22.8 MiB
2025-07-07T15:02:05.329944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length28
Mean length27.745433
Min length25

Characters and Unicode

Total characters627241
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row{'teacher_userid': 2521381}
2nd row{'teacher_userid': 2521381}
3rd row{'teacher_userid': 2521381}
4th row{'teacher_userid': 2521381}
5th row{'teacher_userid': 2521381}
ValueCountFrequency (%)
teacher_userid 22607
50.0%
2521381 4209
 
9.3%
18536109 3760
 
8.3%
12984297 3245
 
7.2%
8227960 1766
 
3.9%
31786504 1284
 
2.8%
98766560 988
 
2.2%
98766896 684
 
1.5%
78523461 614
 
1.4%
99116056 580
 
1.3%
Other values (69) 5477
 
12.1%
2025-07-07T15:02:05.458290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 67821
 
10.8%
' 45214
 
7.2%
r 45214
 
7.2%
1 27535
 
4.4%
{ 22607
 
3.6%
s 22607
 
3.6%
} 22607
 
3.6%
22607
 
3.6%
d 22607
 
3.6%
i 22607
 
3.6%
Other values (16) 305815
48.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 627241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 67821
 
10.8%
' 45214
 
7.2%
r 45214
 
7.2%
1 27535
 
4.4%
{ 22607
 
3.6%
s 22607
 
3.6%
} 22607
 
3.6%
22607
 
3.6%
d 22607
 
3.6%
i 22607
 
3.6%
Other values (16) 305815
48.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 627241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 67821
 
10.8%
' 45214
 
7.2%
r 45214
 
7.2%
1 27535
 
4.4%
{ 22607
 
3.6%
s 22607
 
3.6%
} 22607
 
3.6%
22607
 
3.6%
d 22607
 
3.6%
i 22607
 
3.6%
Other values (16) 305815
48.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 627241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 67821
 
10.8%
' 45214
 
7.2%
r 45214
 
7.2%
1 27535
 
4.4%
{ 22607
 
3.6%
s 22607
 
3.6%
} 22607
 
3.6%
22607
 
3.6%
d 22607
 
3.6%
i 22607
 
3.6%
Other values (16) 305815
48.8%

protocol_set_name
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing644866
Missing (%)90.1%
Memory size39.1 MiB
Surface Temperature
71165 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1352135
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSurface Temperature
2nd rowSurface Temperature
3rd rowSurface Temperature
4th rowSurface Temperature
5th rowSurface Temperature

Common Values

ValueCountFrequency (%)
Surface Temperature 71165
 
9.9%
(Missing) 644866
90.1%

Length

2025-07-07T15:02:05.492855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:05.511787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
surface 71165
50.0%
temperature 71165
50.0%

Most occurring characters

ValueCountFrequency (%)
e 284660
21.1%
r 213495
15.8%
u 142330
10.5%
a 142330
10.5%
S 71165
 
5.3%
f 71165
 
5.3%
c 71165
 
5.3%
71165
 
5.3%
T 71165
 
5.3%
m 71165
 
5.3%
Other values (2) 142330
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1352135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 284660
21.1%
r 213495
15.8%
u 142330
10.5%
a 142330
10.5%
S 71165
 
5.3%
f 71165
 
5.3%
c 71165
 
5.3%
71165
 
5.3%
T 71165
 
5.3%
m 71165
 
5.3%
Other values (2) 142330
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1352135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 284660
21.1%
r 213495
15.8%
u 142330
10.5%
a 142330
10.5%
S 71165
 
5.3%
f 71165
 
5.3%
c 71165
 
5.3%
71165
 
5.3%
T 71165
 
5.3%
m 71165
 
5.3%
Other values (2) 142330
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1352135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 284660
21.1%
r 213495
15.8%
u 142330
10.5%
a 142330
10.5%
S 71165
 
5.3%
f 71165
 
5.3%
c 71165
 
5.3%
71165
 
5.3%
T 71165
 
5.3%
m 71165
 
5.3%
Other values (2) 142330
10.5%

protocol_set_code
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing644866
Missing (%)90.1%
Memory size38.0 MiB
9808
71165 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters284660
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9808
2nd row9808
3rd row9808
4th row9808
5th row9808

Common Values

ValueCountFrequency (%)
9808 71165
 
9.9%
(Missing) 644866
90.1%

Length

2025-07-07T15:02:05.535206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:05.554490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9808 71165
100.0%

Most occurring characters

ValueCountFrequency (%)
8 142330
50.0%
9 71165
25.0%
0 71165
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 284660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 142330
50.0%
9 71165
25.0%
0 71165
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 284660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 142330
50.0%
9 71165
25.0%
0 71165
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 284660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 142330
50.0%
9 71165
25.0%
0 71165
25.0%
Distinct4144
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size55.3 MiB
2025-07-07T15:02:05.671367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length97
Median length60
Mean length25.068814
Min length1

Characters and Unicode

Total characters17950048
Distinct characters287
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique387 ?
Unique (%)0.1%

Sample

1st rowGim7RZ/PL/143:ATM189
2nd rowGim7RZ/PL/143:ATM189
3rd rowGim7RZ/PL/143:ATM189
4th rowGim7RZ/PL/143:ATM189
5th rowGim7RZ/PL/143:ATM189
ValueCountFrequency (%)
school 127186
 
5.3%
96051
 
4.0%
area 47235
 
2.0%
playground 43706
 
1.8%
high 43588
 
1.8%
of 41954
 
1.7%
parking 38509
 
1.6%
surface 30063
 
1.2%
junior 29989
 
1.2%
1 29188
 
1.2%
Other values (4630) 1882996
78.1%
2025-07-07T15:02:05.932341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1696720
 
9.5%
a 1105993
 
6.2%
e 1014647
 
5.7%
o 1012165
 
5.6%
r 816614
 
4.5%
i 746107
 
4.2%
t 692403
 
3.9%
l 634619
 
3.5%
n 634315
 
3.5%
A 601838
 
3.4%
Other values (277) 8994627
50.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17950048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1696720
 
9.5%
a 1105993
 
6.2%
e 1014647
 
5.7%
o 1012165
 
5.6%
r 816614
 
4.5%
i 746107
 
4.2%
t 692403
 
3.9%
l 634619
 
3.5%
n 634315
 
3.5%
A 601838
 
3.4%
Other values (277) 8994627
50.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17950048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1696720
 
9.5%
a 1105993
 
6.2%
e 1014647
 
5.7%
o 1012165
 
5.6%
r 816614
 
4.5%
i 746107
 
4.2%
t 692403
 
3.9%
l 634619
 
3.5%
n 634315
 
3.5%
A 601838
 
3.4%
Other values (277) 8994627
50.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17950048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1696720
 
9.5%
a 1105993
 
6.2%
e 1014647
 
5.7%
o 1012165
 
5.6%
r 816614
 
4.5%
i 746107
 
4.2%
t 692403
 
3.9%
l 634619
 
3.5%
n 634315
 
3.5%
A 601838
 
3.4%
Other values (277) 8994627
50.1%
Distinct2151
Distinct (%)0.3%
Missing705
Missing (%)0.1%
Memory size5.5 MiB
Minimum1996-05-20 00:00:00
Maximum2025-03-27 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T15:02:05.975910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:02:06.017652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

site_deactivated_at
Date

Missing 

Distinct7
Distinct (%)1.1%
Missing715409
Missing (%)99.9%
Memory size5.5 MiB
Minimum2018-05-25 00:00:00
Maximum2024-06-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T15:02:06.049103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:02:06.079293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)

site_comments
Text

Missing 

Distinct679
Distinct (%)0.4%
Missing545001
Missing (%)76.1%
Memory size62.3 MiB
2025-07-07T15:02:06.244453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length500
Median length356
Mean length153.3227
Min length1

Characters and Unicode

Total characters26222782
Distinct characters162
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)< 0.1%

Sample

1st rowThe defined area is a rectangular swath in a parking lot. It was the only available area with ready access for measurement.
2nd rowThe defined area is a rectangular swath in a parking lot. It was the only available area with ready access for measurement.
3rd rowThe defined area is a rectangular swath in a parking lot. It was the only available area with ready access for measurement.
4th rowThe defined area is a rectangular swath in a parking lot. It was the only available area with ready access for measurement.
5th rowThe defined area is a rectangular swath in a parking lot. It was the only available area with ready access for measurement.
ValueCountFrequency (%)
the 328915
 
7.0%
is 177787
 
3.8%
a 147902
 
3.1%
area 117304
 
2.5%
side 100574
 
2.1%
of 95832
 
2.0%
and 85758
 
1.8%
by 73704
 
1.6%
bordered 67903
 
1.4%
school 63910
 
1.4%
Other values (1923) 3442879
73.2%
2025-07-07T15:02:06.454724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4769425
18.2%
e 2655944
 
10.1%
a 1887691
 
7.2%
s 1621509
 
6.2%
r 1517464
 
5.8%
t 1487541
 
5.7%
o 1473032
 
5.6%
i 1275784
 
4.9%
d 1089510
 
4.2%
n 899880
 
3.4%
Other values (152) 7545002
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26222782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4769425
18.2%
e 2655944
 
10.1%
a 1887691
 
7.2%
s 1621509
 
6.2%
r 1517464
 
5.8%
t 1487541
 
5.7%
o 1473032
 
5.6%
i 1275784
 
4.9%
d 1089510
 
4.2%
n 899880
 
3.4%
Other values (152) 7545002
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26222782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4769425
18.2%
e 2655944
 
10.1%
a 1887691
 
7.2%
s 1621509
 
6.2%
r 1517464
 
5.8%
t 1487541
 
5.7%
o 1473032
 
5.6%
i 1275784
 
4.9%
d 1089510
 
4.2%
n 899880
 
3.4%
Other values (152) 7545002
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26222782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4769425
18.2%
e 2655944
 
10.1%
a 1887691
 
7.2%
s 1621509
 
6.2%
r 1517464
 
5.8%
t 1487541
 
5.7%
o 1473032
 
5.6%
i 1275784
 
4.9%
d 1089510
 
4.2%
n 899880
 
3.4%
Other values (152) 7545002
28.8%

site_latitude
Real number (ℝ)

High correlation 

Distinct3727
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.517707
Minimum-62.767504
Maximum89.999997
Zeros426
Zeros (%)0.1%
Negative13212
Negative (%)1.8%
Memory size5.5 MiB
2025-07-07T15:02:06.497953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-62.767504
5-th percentile18.785322
Q125.225961
median40.2464
Q341.92
95-th percentile50.4515
Maximum89.999997
Range152.7675
Interquartile range (IQR)16.694039

Descriptive statistics

Standard deviation13.2579
Coefficient of variation (CV)0.37327579
Kurtosis7.9260145
Mean35.517707
Median Absolute Deviation (MAD)5.309526
Skewness-1.7979026
Sum25431779
Variance175.77191
MonotonicityNot monotonic
2025-07-07T15:02:06.538825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.65667 20676
 
2.9%
22.630026 20555
 
2.9%
22.713529 18392
 
2.6%
48.92 15920
 
2.2%
25.086605 14555
 
2.0%
41.24375 13622
 
1.9%
46.300264 13186
 
1.8%
41.2439 13091
 
1.8%
36.290455 11663
 
1.6%
22.996032 10912
 
1.5%
Other values (3717) 563459
78.7%
ValueCountFrequency (%)
-62.767504 1
 
< 0.1%
-44.0488 17
 
< 0.1%
-43.12097 54
< 0.1%
-40.148372 9
 
< 0.1%
-39.99519 1
 
< 0.1%
-39.980476 1
 
< 0.1%
-39.958297 7
 
< 0.1%
-39.957271 57
< 0.1%
-39.95725 20
 
< 0.1%
-39.957043 14
 
< 0.1%
ValueCountFrequency (%)
89.999997 985
0.1%
87.72147 54
 
< 0.1%
84.1 2
 
< 0.1%
75.332516 1513
0.2%
68.40352 3
 
< 0.1%
68.36137 1
 
< 0.1%
68.32821 1
 
< 0.1%
68.29618 1
 
< 0.1%
66.19 1
 
< 0.1%
64.9474 9
 
< 0.1%

site_longitude
Real number (ℝ)

High correlation 

Distinct3792
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-12.748182
Minimum-161.43865
Maximum145.14711
Zeros719
Zeros (%)0.1%
Negative368352
Negative (%)51.4%
Memory size5.5 MiB
2025-07-07T15:02:06.578038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-161.43865
5-th percentile-92.42271
Q1-82.61141
median-17.90446
Q335.2618
95-th percentile121.49166
Maximum145.14711
Range306.58576
Interquartile range (IQR)117.87321

Descriptive statistics

Standard deviation76.311984
Coefficient of variation (CV)-5.9861073
Kurtosis-1.0803609
Mean-12.748182
Median Absolute Deviation (MAD)64.33537
Skewness0.48587326
Sum-9128093.4
Variance5823.519
MonotonicityNot monotonic
2025-07-07T15:02:06.616425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-17.90446 20676
 
2.9%
120.291204 20555
 
2.9%
120.286628 18392
 
2.6%
24.69 15920
 
2.2%
121.49166 14555
 
2.0%
-82.6116 13622
 
1.9%
16.328929 13186
 
1.8%
-82.61141 13091
 
1.8%
-93.296115 11663
 
1.6%
120.224649 10912
 
1.5%
Other values (3782) 563459
78.7%
ValueCountFrequency (%)
-161.43865 80
 
< 0.1%
-161.011 330
< 0.1%
-160.561 207
 
< 0.1%
-160.401 155
 
< 0.1%
-159.56917 49
 
< 0.1%
-159.532974 766
0.1%
-159.5328 4
 
< 0.1%
-159.2328 405
0.1%
-149.41118 81
 
< 0.1%
-148.3202 9
 
< 0.1%
ValueCountFrequency (%)
145.14711 27
 
< 0.1%
136.9552 2
 
< 0.1%
135.26091 3
 
< 0.1%
128.557512 225
 
< 0.1%
127.821747 1524
0.2%
127.423323 12
 
< 0.1%
127.38938 189
 
< 0.1%
127.384619 60
 
< 0.1%
127.3288 1
 
< 0.1%
127.124891 35
 
< 0.1%

site_elevation
Real number (ℝ)

High correlation 

Distinct2069
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.85125
Minimum-5111.3
Maximum8000
Zeros870
Zeros (%)0.1%
Negative2462
Negative (%)0.3%
Memory size5.5 MiB
2025-07-07T15:02:06.655423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5111.3
5-th percentile6.3
Q151.2
median188.3
Q3296.4
95-th percentile535
Maximum8000
Range13111.3
Interquartile range (IQR)245.2

Descriptive statistics

Standard deviation279.09097
Coefficient of variation (CV)1.3112019
Kurtosis111.44121
Mean212.85125
Median Absolute Deviation (MAD)130.3
Skewness-0.73814331
Sum1.5240809 × 108
Variance77891.767
MonotonicityNot monotonic
2025-07-07T15:02:06.695611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
354.5 20676
 
2.9%
6.8 20564
 
2.9%
15 19345
 
2.7%
260 15958
 
2.2%
6 14797
 
2.1%
331.4 13622
 
1.9%
171 13262
 
1.9%
171.7 13186
 
1.8%
296.4 13091
 
1.8%
378 12171
 
1.7%
Other values (2059) 559359
78.1%
ValueCountFrequency (%)
-5111.3 183
 
< 0.1%
-5071.3 2
 
< 0.1%
-4228.9 5
 
< 0.1%
-4011.6 9
 
< 0.1%
-3492 722
0.1%
-2851.8 1
 
< 0.1%
-1040.8 12
 
< 0.1%
-393.2 79
 
< 0.1%
-215.6 180
 
< 0.1%
-215.5 180
 
< 0.1%
ValueCountFrequency (%)
8000 5
 
< 0.1%
7730 27
< 0.1%
7670 6
 
< 0.1%
6588 4
 
< 0.1%
6035 10
 
< 0.1%
5724.4 7
 
< 0.1%
3088 3
 
< 0.1%
3022.1 30
< 0.1%
3021.9 1
 
< 0.1%
3021.5 1
 
< 0.1%

site_elevation_type
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing484872
Missing (%)67.7%
Memory size39.1 MiB
ellipsoidal
207258 
orthometric
23901 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters2542749
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowellipsoidal
2nd rowellipsoidal
3rd rowellipsoidal
4th rowellipsoidal
5th rowellipsoidal

Common Values

ValueCountFrequency (%)
ellipsoidal 207258
28.9%
orthometric 23901
 
3.3%
(Missing) 484872
67.7%

Length

2025-07-07T15:02:06.729310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:06.747864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ellipsoidal 207258
89.7%
orthometric 23901
 
10.3%

Most occurring characters

ValueCountFrequency (%)
l 621774
24.5%
i 438417
17.2%
o 255060
10.0%
e 231159
 
9.1%
p 207258
 
8.2%
s 207258
 
8.2%
d 207258
 
8.2%
a 207258
 
8.2%
r 47802
 
1.9%
t 47802
 
1.9%
Other values (3) 71703
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2542749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 621774
24.5%
i 438417
17.2%
o 255060
10.0%
e 231159
 
9.1%
p 207258
 
8.2%
s 207258
 
8.2%
d 207258
 
8.2%
a 207258
 
8.2%
r 47802
 
1.9%
t 47802
 
1.9%
Other values (3) 71703
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2542749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 621774
24.5%
i 438417
17.2%
o 255060
10.0%
e 231159
 
9.1%
p 207258
 
8.2%
s 207258
 
8.2%
d 207258
 
8.2%
a 207258
 
8.2%
r 47802
 
1.9%
t 47802
 
1.9%
Other values (3) 71703
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2542749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 621774
24.5%
i 438417
17.2%
o 255060
10.0%
e 231159
 
9.1%
p 207258
 
8.2%
s 207258
 
8.2%
d 207258
 
8.2%
a 207258
 
8.2%
r 47802
 
1.9%
t 47802
 
1.9%
Other values (3) 71703
 
2.8%

site_location_source
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.9 MiB
gps
511951 
other
160986 
school
 
33617
auto
 
9477

Length

Max length6
Median length3
Mean length3.6037448
Min length3

Characters and Unicode

Total characters2580393
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgps
2nd rowgps
3rd rowgps
4th rowgps
5th rowgps

Common Values

ValueCountFrequency (%)
gps 511951
71.5%
other 160986
 
22.5%
school 33617
 
4.7%
auto 9477
 
1.3%

Length

2025-07-07T15:02:06.775376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:06.797921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
gps 511951
71.5%
other 160986
 
22.5%
school 33617
 
4.7%
auto 9477
 
1.3%

Most occurring characters

ValueCountFrequency (%)
s 545568
21.1%
g 511951
19.8%
p 511951
19.8%
o 237697
9.2%
h 194603
 
7.5%
t 170463
 
6.6%
e 160986
 
6.2%
r 160986
 
6.2%
c 33617
 
1.3%
l 33617
 
1.3%
Other values (2) 18954
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2580393
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 545568
21.1%
g 511951
19.8%
p 511951
19.8%
o 237697
9.2%
h 194603
 
7.5%
t 170463
 
6.6%
e 160986
 
6.2%
r 160986
 
6.2%
c 33617
 
1.3%
l 33617
 
1.3%
Other values (2) 18954
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2580393
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 545568
21.1%
g 511951
19.8%
p 511951
19.8%
o 237697
9.2%
h 194603
 
7.5%
t 170463
 
6.6%
e 160986
 
6.2%
r 160986
 
6.2%
c 33617
 
1.3%
l 33617
 
1.3%
Other values (2) 18954
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2580393
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 545568
21.1%
g 511951
19.8%
p 511951
19.8%
o 237697
9.2%
h 194603
 
7.5%
t 170463
 
6.6%
e 160986
 
6.2%
r 160986
 
6.2%
c 33617
 
1.3%
l 33617
 
1.3%
Other values (2) 18954
 
0.7%
Distinct4339
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size78.5 MiB
2025-07-07T15:02:07.128864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters47258046
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique698 ?
Unique (%)0.1%

Sample

1st row01010000A0E6100000780B24287E2C3640FBCBEEC9C31249403333333333636640
2nd row01010000A0E6100000780B24287E2C3640FBCBEEC9C31249403333333333636640
3rd row01010000A0E6100000780B24287E2C3640FBCBEEC9C31249403333333333636640
4th row01010000A0E6100000780B24287E2C3640FBCBEEC9C31249403333333333636640
5th row01010000A0E6100000780B24287E2C3640FBCBEEC9C31249403333333333636640
ValueCountFrequency (%)
01010000a0e6100000488ac8b08ae731c0aa436e861ba83c400000000000287640 20676
 
2.9%
01010000a0e6100000b81d1a16a3125e4034a1496249a136403333333333331b40 20555
 
2.9%
01010000a0e61000008b87f71c58125e4062bf27d6a9b636400000000000002e40 18392
 
2.6%
01010000a0e6100000713d0ad7a3b03840f6285c8fc27548400000000000407040 15920
 
2.2%
01010000a0e61000001630815b775f5e408aabcabe2b1639400000000000001840 14555
 
2.0%
01010000a0e6100000f38e537424a754c033333333339f44406666666666b67440 13622
 
1.9%
01010000a0e6100000bbb4e1b0345430404415fe0c6f2647400000000000005e40 13186
 
1.8%
01010000a0e6100000a29c685721a754c048bf7d1d389f44406666666666867240 13091
 
1.8%
01010000a0e6100000b936548cf35257c0d7fa22a12d2542400000000000a07740 11663
 
1.6%
01010000a0e6100000100533a6600e5e40fcc401f4fbfe36406666666666663840 10912
 
1.5%
Other values (4329) 563459
78.7%
2025-07-07T15:02:07.505930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16619333
35.2%
4 4049760
 
8.6%
1 3526574
 
7.5%
6 3344260
 
7.1%
3 2846330
 
6.0%
C 1980703
 
4.2%
A 1979493
 
4.2%
E 1918764
 
4.1%
5 1753615
 
3.7%
9 1573937
 
3.3%
Other values (6) 7665277
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47258046
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16619333
35.2%
4 4049760
 
8.6%
1 3526574
 
7.5%
6 3344260
 
7.1%
3 2846330
 
6.0%
C 1980703
 
4.2%
A 1979493
 
4.2%
E 1918764
 
4.1%
5 1753615
 
3.7%
9 1573937
 
3.3%
Other values (6) 7665277
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47258046
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16619333
35.2%
4 4049760
 
8.6%
1 3526574
 
7.5%
6 3344260
 
7.1%
3 2846330
 
6.0%
C 1980703
 
4.2%
A 1979493
 
4.2%
E 1918764
 
4.1%
5 1753615
 
3.7%
9 1573937
 
3.3%
Other values (6) 7665277
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47258046
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16619333
35.2%
4 4049760
 
8.6%
1 3526574
 
7.5%
6 3344260
 
7.1%
3 2846330
 
6.0%
C 1980703
 
4.2%
A 1979493
 
4.2%
E 1918764
 
4.1%
5 1753615
 
3.7%
9 1573937
 
3.3%
Other values (6) 7665277
16.2%

site_developer_key_id
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing705
Missing (%)0.1%
Memory size34.1 MiB
1
583099 
5
125752 
4
 
6468
7
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters715326
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 583099
81.4%
5 125752
 
17.6%
4 6468
 
0.9%
7 7
 
< 0.1%
(Missing) 705
 
0.1%

Length

2025-07-07T15:02:07.548354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:07.573512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 583099
81.5%
5 125752
 
17.6%
4 6468
 
0.9%
7 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 583099
81.5%
5 125752
 
17.6%
4 6468
 
0.9%
7 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 715326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 583099
81.5%
5 125752
 
17.6%
4 6468
 
0.9%
7 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 715326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 583099
81.5%
5 125752
 
17.6%
4 6468
 
0.9%
7 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 715326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 583099
81.5%
5 125752
 
17.6%
4 6468
 
0.9%
7 7
 
< 0.1%

site_is_citizen_science
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
False
715959 
True
 
72
ValueCountFrequency (%)
False 715959
> 99.9%
True 72
 
< 0.1%
2025-07-07T15:02:07.590961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

site_nickname
Unsupported

Missing  Rejected  Unsupported 

Missing716031
Missing (%)100.0%
Memory size38.2 MiB

site_true_latitude
Real number (ℝ)

High correlation  Missing 

Distinct12
Distinct (%)14.3%
Missing715947
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean25.645267
Minimum-0.00112
Maximum41.65432
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)< 0.1%
Memory size5.5 MiB
2025-07-07T15:02:07.610202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.00112
5-th percentile-0.00112
Q125.9985
median32.0485
Q332.0485
95-th percentile32.0545
Maximum41.65432
Range41.65544
Interquartile range (IQR)6.05

Descriptive statistics

Standard deviation11.741546
Coefficient of variation (CV)0.45784454
Kurtosis0.27354957
Mean25.645267
Median Absolute Deviation (MAD)0
Skewness-1.386739
Sum2154.2025
Variance137.86389
MonotonicityNot monotonic
2025-07-07T15:02:07.635915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
32.0485 45
 
< 0.1%
-0.00112 9
 
< 0.1%
7.88617 9
 
< 0.1%
32.0545 9
 
< 0.1%
30.4199 3
 
< 0.1%
25.9954 2
 
< 0.1%
25.9985 2
 
< 0.1%
41.65432 1
 
< 0.1%
26.003 1
 
< 0.1%
26.0017 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 715947
> 99.9%
ValueCountFrequency (%)
-0.00112 9
 
< 0.1%
7.88617 9
 
< 0.1%
25.9954 2
 
< 0.1%
25.9985 2
 
< 0.1%
26.0008 1
 
< 0.1%
26.0017 1
 
< 0.1%
26.003 1
 
< 0.1%
30.4199 3
 
< 0.1%
32.0485 45
< 0.1%
32.0545 9
 
< 0.1%
ValueCountFrequency (%)
41.65432 1
 
< 0.1%
37.6567 1
 
< 0.1%
32.0545 9
 
< 0.1%
32.0485 45
< 0.1%
30.4199 3
 
< 0.1%
26.003 1
 
< 0.1%
26.0017 1
 
< 0.1%
26.0008 1
 
< 0.1%
25.9985 2
 
< 0.1%
25.9954 2
 
< 0.1%

site_true_longitude
Real number (ℝ)

High correlation  Missing 

Distinct13
Distinct (%)15.5%
Missing715947
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean15.198454
Minimum-97.7796
Maximum79.1666
Zeros0
Zeros (%)0.0%
Negative14
Negative (%)< 0.1%
Memory size5.5 MiB
2025-07-07T15:02:07.659810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-97.7796
5-th percentile-82.395412
Q10.0133
median34.7701
Q334.7701
95-th percentile79.1619
Maximum79.1666
Range176.9462
Interquartile range (IQR)34.7568

Descriptive statistics

Standard deviation47.370483
Coefficient of variation (CV)3.1167961
Kurtosis0.66439669
Mean15.198454
Median Absolute Deviation (MAD)0.0003
Skewness-1.299135
Sum1276.6701
Variance2243.9626
MonotonicityNot monotonic
2025-07-07T15:02:07.687346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
34.7701 36
 
< 0.1%
0.0133 9
 
< 0.1%
34.7698 9
 
< 0.1%
-76.63747 9
 
< 0.1%
34.8316 9
 
< 0.1%
-97.7796 3
 
< 0.1%
79.1619 2
 
< 0.1%
79.1623 2
 
< 0.1%
-83.41152 1
 
< 0.1%
79.1666 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 715947
> 99.9%
ValueCountFrequency (%)
-97.7796 3
 
< 0.1%
-89.2371 1
 
< 0.1%
-83.41152 1
 
< 0.1%
-76.63747 9
 
< 0.1%
0.0133 9
 
< 0.1%
34.7698 9
 
< 0.1%
34.7701 36
< 0.1%
34.8316 9
 
< 0.1%
79.1612 1
 
< 0.1%
79.1619 2
 
< 0.1%
ValueCountFrequency (%)
79.1666 1
 
< 0.1%
79.1627 1
 
< 0.1%
79.1623 2
 
< 0.1%
79.1619 2
 
< 0.1%
79.1612 1
 
< 0.1%
34.8316 9
 
< 0.1%
34.7701 36
< 0.1%
34.7698 9
 
< 0.1%
0.0133 9
 
< 0.1%
-76.63747 9
 
< 0.1%

site_true_elevation
Real number (ℝ)

High correlation  Missing 

Distinct13
Distinct (%)15.5%
Missing715947
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean-382.26071
Minimum-4011.6
Maximum293
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)< 0.1%
Memory size5.5 MiB
2025-07-07T15:02:07.713752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4011.6
5-th percentile-4011.6
Q124.5
median24.5
Q329.575
95-th percentile176.135
Maximum293
Range4304.6
Interquartile range (IQR)5.075

Descriptive statistics

Standard deviation1266.3534
Coefficient of variation (CV)-3.3128003
Kurtosis4.7686187
Mean-382.26071
Median Absolute Deviation (MAD)1.8
Skewness-2.5742784
Sum-32109.9
Variance1603651
MonotonicityNot monotonic
2025-07-07T15:02:07.740802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
24.5 36
 
< 0.1%
-4011.6 9
 
< 0.1%
22.7 9
 
< 0.1%
27.7 9
 
< 0.1%
35.2 9
 
< 0.1%
293 3
 
< 0.1%
158.2 2
 
< 0.1%
157.1 2
 
< 0.1%
179.3 1
 
< 0.1%
152.7 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 715947
> 99.9%
ValueCountFrequency (%)
-4011.6 9
 
< 0.1%
22.7 9
 
< 0.1%
24.5 36
< 0.1%
27.7 9
 
< 0.1%
35.2 9
 
< 0.1%
152.7 1
 
< 0.1%
153.9 1
 
< 0.1%
157.1 2
 
< 0.1%
157.6 1
 
< 0.1%
158.2 2
 
< 0.1%
ValueCountFrequency (%)
293 3
 
< 0.1%
189 1
 
< 0.1%
179.3 1
 
< 0.1%
158.2 2
 
< 0.1%
157.6 1
 
< 0.1%
157.1 2
 
< 0.1%
153.9 1
 
< 0.1%
152.7 1
 
< 0.1%
35.2 9
< 0.1%
27.7 9
< 0.1%

site_true_point
Categorical

High correlation  Missing 

Distinct13
Distinct (%)15.5%
Missing715947
Missing (%)> 99.9%
Memory size38.2 MiB
01010000A0E6100000265305A39262414091ED7C3F350640400000000000803840
36 
01010000A0E6100000CC5D4BC8073D8B3FD2FBC6D79E5952BF333333333357AFC0
01010000A0E6100000FB3A70CE8862414091ED7C3F350640403333333333B33640
01010000A0E6100000978BF84ECC2853C008C90226708B1F403333333333B33B40
01010000A0E610000043AD69DE716A4140E5D022DBF90640409A99999999994140
Other values (8)
12 

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters5544
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)6.0%

Sample

1st row01010000A0E6100000CC5D4BC8073D8B3FD2FBC6D79E5952BF333333333357AFC0
2nd row01010000A0E6100000CC5D4BC8073D8B3FD2FBC6D79E5952BF333333333357AFC0
3rd row01010000A0E6100000CC5D4BC8073D8B3FD2FBC6D79E5952BF333333333357AFC0
4th row01010000A0E6100000CC5D4BC8073D8B3FD2FBC6D79E5952BF333333333357AFC0
5th row01010000A0E6100000CC5D4BC8073D8B3FD2FBC6D79E5952BF333333333357AFC0

Common Values

ValueCountFrequency (%)
01010000A0E6100000265305A39262414091ED7C3F350640400000000000803840 36
 
< 0.1%
01010000A0E6100000CC5D4BC8073D8B3FD2FBC6D79E5952BF333333333357AFC0 9
 
< 0.1%
01010000A0E6100000FB3A70CE8862414091ED7C3F350640403333333333B33640 9
 
< 0.1%
01010000A0E6100000978BF84ECC2853C008C90226708B1F403333333333B33B40 9
 
< 0.1%
01010000A0E610000043AD69DE716A4140E5D022DBF90640409A99999999994140 9
 
< 0.1%
01010000A0E61000008BFD65F7E47158C02497FF907E6B3E400000000000507240 3
 
< 0.1%
01010000A0E61000003C4ED1915CCA53403B70CE88D2FE39406666666666C66340 2
 
< 0.1%
01010000A0E610000003098A1F63CA5340560E2DB29DFF39403333333333A36340 2
 
< 0.1%
01010000A0E61000009869FB5756DA54C0328FFCC1C0D344409A99999999696640 1
 
< 0.1%
01010000A0E6100000DFE00B93A9CA534054E3A59BC4003A406666666666166340 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 715947
> 99.9%

Length

2025-07-07T15:02:07.768065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01010000a0e6100000265305a39262414091ed7c3f350640400000000000803840 36
42.9%
01010000a0e6100000cc5d4bc8073d8b3fd2fbc6d79e5952bf333333333357afc0 9
 
10.7%
01010000a0e6100000fb3a70ce8862414091ed7c3f350640403333333333b33640 9
 
10.7%
01010000a0e6100000978bf84ecc2853c008c90226708b1f403333333333b33b40 9
 
10.7%
01010000a0e610000043ad69de716a4140e5d022dbf90640409a99999999994140 9
 
10.7%
01010000a0e61000008bfd65f7e47158c02497ff907e6b3e400000000000507240 3
 
3.6%
01010000a0e61000003c4ed1915cca53403b70ce88d2fe39406666666666c66340 2
 
2.4%
01010000a0e610000003098a1f63ca5340560e2db29dff39403333333333a36340 2
 
2.4%
01010000a0e61000009869fb5756da54c0328ffcc1c0d344409a99999999696640 1
 
1.2%
01010000a0e6100000dfe00b93a9ca534054e3a59bc4003a406666666666166340 1
 
1.2%
Other values (3) 3
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 1903
34.3%
3 623
 
11.2%
1 391
 
7.1%
4 377
 
6.8%
6 328
 
5.9%
9 269
 
4.9%
5 201
 
3.6%
E 196
 
3.5%
2 196
 
3.5%
A 189
 
3.4%
Other values (6) 871
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1903
34.3%
3 623
 
11.2%
1 391
 
7.1%
4 377
 
6.8%
6 328
 
5.9%
9 269
 
4.9%
5 201
 
3.6%
E 196
 
3.5%
2 196
 
3.5%
A 189
 
3.4%
Other values (6) 871
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1903
34.3%
3 623
 
11.2%
1 391
 
7.1%
4 377
 
6.8%
6 328
 
5.9%
9 269
 
4.9%
5 201
 
3.6%
E 196
 
3.5%
2 196
 
3.5%
A 189
 
3.4%
Other values (6) 871
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1903
34.3%
3 623
 
11.2%
1 391
 
7.1%
4 377
 
6.8%
6 328
 
5.9%
9 269
 
4.9%
5 201
 
3.6%
E 196
 
3.5%
2 196
 
3.5%
A 189
 
3.4%
Other values (6) 871
15.7%
Distinct1059
Distinct (%)1.1%
Missing621473
Missing (%)86.8%
Memory size5.5 MiB
Minimum2002-01-09 00:00:00
Maximum2025-02-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-07T15:02:07.802936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:02:07.847257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1165
Distinct (%)1.2%
Missing621477
Missing (%)86.8%
Memory size43.3 MiB
https://data.globe.gov/system/photos/2016/03/21/30417/thumb.jpg
 
18239
https://data.globe.gov/system/photos/2018/10/17/877470/thumb.jpg
 
11663
https://data.globe.gov/system/photos/2014/08/25/5818/thumb.jpg
 
10406
https://data.globe.gov/system/photos/2018/11/03/902416/thumb.jpg
 
7453
https://data.globe.gov/system/photos/2004/09/28/373/thumb.jpg
 
5132
Other values (1160)
 
41661
(Missing)
621477 
ValueCountFrequency (%)
https://data.globe.gov/system/photos/2016/03/21/30417/thumb.jpg 18239
 
2.5%
https://data.globe.gov/system/photos/2018/10/17/877470/thumb.jpg 11663
 
1.6%
https://data.globe.gov/system/photos/2014/08/25/5818/thumb.jpg 10406
 
1.5%
https://data.globe.gov/system/photos/2018/11/03/902416/thumb.jpg 7453
 
1.0%
https://data.globe.gov/system/photos/2004/09/28/373/thumb.jpg 5132
 
0.7%
https://data.globe.gov/system/photos/2015/08/11/17284/thumb.jpg 4697
 
0.7%
https://data.globe.gov/system/photos/2020/04/07/1997969/thumb.jpg 2907
 
0.4%
https://data.globe.gov/system/photos/2021/08/17/2382687/thumb.jpg 2395
 
0.3%
https://data.globe.gov/system/photos/2020/07/09/1908913/thumb.jpg 1980
 
0.3%
https://data.globe.gov/system/photos/2014/08/28/8556/thumb.jpg 1873
 
0.3%
Other values (1155) 27809
 
3.9%
(Missing) 621477
86.8%
ValueCountFrequency (%)
https 94554
 
13.2%
(Missing) 621477
86.8%
ValueCountFrequency (%)
data.globe.gov 94554
 
13.2%
(Missing) 621477
86.8%
ValueCountFrequency (%)
/system/photos/2016/03/21/30417/thumb.jpg 18239
 
2.5%
/system/photos/2018/10/17/877470/thumb.jpg 11663
 
1.6%
/system/photos/2014/08/25/5818/thumb.jpg 10406
 
1.5%
/system/photos/2018/11/03/902416/thumb.jpg 7453
 
1.0%
/system/photos/2004/09/28/373/thumb.jpg 5132
 
0.7%
/system/photos/2015/08/11/17284/thumb.jpg 4697
 
0.7%
/system/photos/2020/04/07/1997969/thumb.jpg 2907
 
0.4%
/system/photos/2021/08/17/2382687/thumb.jpg 2395
 
0.3%
/system/photos/2020/07/09/1908913/thumb.jpg 1980
 
0.3%
/system/photos/2014/08/28/8556/thumb.jpg 1873
 
0.3%
Other values (1155) 27809
 
3.9%
(Missing) 621477
86.8%
ValueCountFrequency (%)
94554
 
13.2%
(Missing) 621477
86.8%
ValueCountFrequency (%)
94554
 
13.2%
(Missing) 621477
86.8%
Distinct1165
Distinct (%)1.2%
Missing621477
Missing (%)86.8%
Memory size43.6 MiB
https://data.globe.gov/system/photos/2016/03/21/30417/original.jpg
 
18239
https://data.globe.gov/system/photos/2018/10/17/877470/original.jpg
 
11663
https://data.globe.gov/system/photos/2014/08/25/5818/original.jpg
 
10406
https://data.globe.gov/system/photos/2018/11/03/902416/original.jpg
 
7453
https://data.globe.gov/system/photos/2004/09/28/373/original.JPG
 
5132
Other values (1160)
 
41661
(Missing)
621477 
ValueCountFrequency (%)
https://data.globe.gov/system/photos/2016/03/21/30417/original.jpg 18239
 
2.5%
https://data.globe.gov/system/photos/2018/10/17/877470/original.jpg 11663
 
1.6%
https://data.globe.gov/system/photos/2014/08/25/5818/original.jpg 10406
 
1.5%
https://data.globe.gov/system/photos/2018/11/03/902416/original.jpg 7453
 
1.0%
https://data.globe.gov/system/photos/2004/09/28/373/original.JPG 5132
 
0.7%
https://data.globe.gov/system/photos/2015/08/11/17284/original.jpg 4697
 
0.7%
https://data.globe.gov/system/photos/2020/04/07/1997969/original.jpg 2907
 
0.4%
https://data.globe.gov/system/photos/2021/08/17/2382687/original.jpg 2395
 
0.3%
https://data.globe.gov/system/photos/2020/07/09/1908913/original.jpg 1980
 
0.3%
https://data.globe.gov/system/photos/2014/08/28/8556/original.jpg 1873
 
0.3%
Other values (1155) 27809
 
3.9%
(Missing) 621477
86.8%
ValueCountFrequency (%)
https 94554
 
13.2%
(Missing) 621477
86.8%
ValueCountFrequency (%)
data.globe.gov 94554
 
13.2%
(Missing) 621477
86.8%
ValueCountFrequency (%)
/system/photos/2016/03/21/30417/original.jpg 18239
 
2.5%
/system/photos/2018/10/17/877470/original.jpg 11663
 
1.6%
/system/photos/2014/08/25/5818/original.jpg 10406
 
1.5%
/system/photos/2018/11/03/902416/original.jpg 7453
 
1.0%
/system/photos/2004/09/28/373/original.JPG 5132
 
0.7%
/system/photos/2015/08/11/17284/original.jpg 4697
 
0.7%
/system/photos/2020/04/07/1997969/original.jpg 2907
 
0.4%
/system/photos/2021/08/17/2382687/original.jpg 2395
 
0.3%
/system/photos/2020/07/09/1908913/original.jpg 1980
 
0.3%
/system/photos/2014/08/28/8556/original.jpg 1873
 
0.3%
Other values (1155) 27809
 
3.9%
(Missing) 621477
86.8%
ValueCountFrequency (%)
94554
 
13.2%
(Missing) 621477
86.8%
ValueCountFrequency (%)
94554
 
13.2%
(Missing) 621477
86.8%

site_photo_photo_data
Text

Missing 

Distinct1167
Distinct (%)1.2%
Missing621473
Missing (%)86.8%
Memory size199.0 MiB
2025-07-07T15:02:07.954818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4772
Median length2574
Mean length1788.2473
Min length389

Characters and Unicode

Total characters169093090
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique796 ?
Unique (%)0.8%

Sample

1st row{'North': [{'photo_id': 17308, 'date_taken': '2015-09-10', 'caption': 'North', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/thumb.jpg', 'created_at': '2015-09-11T01:50:45.490917'}], 'East': [{'photo_id': 17309, 'date_taken': '2015-09-10', 'caption': 'East', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/thumb.jpg', 'created_at': '2015-09-11T01:51:03.530762'}], 'South': [{'photo_id': 17310, 'date_taken': '2015-09-10', 'caption': 'South', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/thumb.jpg', 'created_at': '2015-09-11T01:51:17.293862'}], 'West': [{'photo_id': 17311, 'date_taken': '2015-09-10', 'caption': 'West', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/thumb.jpg', 'created_at': '2015-09-11T01:52:13.365594'}]}
2nd row{'North': [{'photo_id': 17308, 'date_taken': '2015-09-10', 'caption': 'North', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/thumb.jpg', 'created_at': '2015-09-11T01:50:45.490917'}], 'East': [{'photo_id': 17309, 'date_taken': '2015-09-10', 'caption': 'East', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/thumb.jpg', 'created_at': '2015-09-11T01:51:03.530762'}], 'South': [{'photo_id': 17310, 'date_taken': '2015-09-10', 'caption': 'South', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/thumb.jpg', 'created_at': '2015-09-11T01:51:17.293862'}], 'West': [{'photo_id': 17311, 'date_taken': '2015-09-10', 'caption': 'West', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/thumb.jpg', 'created_at': '2015-09-11T01:52:13.365594'}]}
3rd row{'North': [{'photo_id': 17308, 'date_taken': '2015-09-10', 'caption': 'North', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/thumb.jpg', 'created_at': '2015-09-11T01:50:45.490917'}], 'East': [{'photo_id': 17309, 'date_taken': '2015-09-10', 'caption': 'East', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/thumb.jpg', 'created_at': '2015-09-11T01:51:03.530762'}], 'South': [{'photo_id': 17310, 'date_taken': '2015-09-10', 'caption': 'South', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/thumb.jpg', 'created_at': '2015-09-11T01:51:17.293862'}], 'West': [{'photo_id': 17311, 'date_taken': '2015-09-10', 'caption': 'West', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/thumb.jpg', 'created_at': '2015-09-11T01:52:13.365594'}]}
4th row{'North': [{'photo_id': 17308, 'date_taken': '2015-09-10', 'caption': 'North', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/thumb.jpg', 'created_at': '2015-09-11T01:50:45.490917'}], 'East': [{'photo_id': 17309, 'date_taken': '2015-09-10', 'caption': 'East', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/thumb.jpg', 'created_at': '2015-09-11T01:51:03.530762'}], 'South': [{'photo_id': 17310, 'date_taken': '2015-09-10', 'caption': 'South', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/thumb.jpg', 'created_at': '2015-09-11T01:51:17.293862'}], 'West': [{'photo_id': 17311, 'date_taken': '2015-09-10', 'caption': 'West', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/thumb.jpg', 'created_at': '2015-09-11T01:52:13.365594'}]}
5th row{'North': [{'photo_id': 17308, 'date_taken': '2015-09-10', 'caption': 'North', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17308/thumb.jpg', 'created_at': '2015-09-11T01:50:45.490917'}], 'East': [{'photo_id': 17309, 'date_taken': '2015-09-10', 'caption': 'East', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17309/thumb.jpg', 'created_at': '2015-09-11T01:51:03.530762'}], 'South': [{'photo_id': 17310, 'date_taken': '2015-09-10', 'caption': 'South', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17310/thumb.jpg', 'created_at': '2015-09-11T01:51:17.293862'}], 'West': [{'photo_id': 17311, 'date_taken': '2015-09-10', 'caption': 'West', 'approval_status': 'approved', 'image_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/original.JPG', 'small_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/small.jpg', 'thumb_url': 'https://data.globe.gov/system/photos/2015/09/10/17311/thumb.jpg', 'created_at': '2015-09-11T01:52:13.365594'}]}
ValueCountFrequency (%)
photo_id 421587
 
5.7%
image_url 421587
 
5.7%
created_at 421587
 
5.7%
date_taken 421587
 
5.7%
thumb_url 421587
 
5.7%
caption 421587
 
5.7%
small_url 421587
 
5.7%
approval_status 421587
 
5.7%
approved 421580
 
5.7%
none 303879
 
4.1%
Other values (32221) 3325936
44.8%
2025-07-07T15:02:08.100040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 12877153
 
7.6%
/ 11382660
 
6.7%
t 11070212
 
6.5%
a 8320359
 
4.9%
o 8232339
 
4.9%
7329533
 
4.3%
s 6937525
 
4.1%
0 6396845
 
3.8%
p 6319327
 
3.7%
1 6038798
 
3.6%
Other values (82) 84188339
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 169093090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 12877153
 
7.6%
/ 11382660
 
6.7%
t 11070212
 
6.5%
a 8320359
 
4.9%
o 8232339
 
4.9%
7329533
 
4.3%
s 6937525
 
4.1%
0 6396845
 
3.8%
p 6319327
 
3.7%
1 6038798
 
3.6%
Other values (82) 84188339
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 169093090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 12877153
 
7.6%
/ 11382660
 
6.7%
t 11070212
 
6.5%
a 8320359
 
4.9%
o 8232339
 
4.9%
7329533
 
4.3%
s 6937525
 
4.1%
0 6396845
 
3.8%
p 6319327
 
3.7%
1 6038798
 
3.6%
Other values (82) 84188339
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 169093090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 12877153
 
7.6%
/ 11382660
 
6.7%
t 11070212
 
6.5%
a 8320359
 
4.9%
o 8232339
 
4.9%
7329533
 
4.3%
s 6937525
 
4.1%
0 6396845
 
3.8%
p 6319327
 
3.7%
1 6038798
 
3.6%
Other values (82) 84188339
49.8%

developer_key_name
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing705
Missing (%)0.1%
Memory size50.2 MiB
GLOBE Data Entry Web Forms
583099 
GLOBE Observer App
125752 
GLOBE Data Entry App
 
6468
GLOBE EMDE SCOOL
 
7

Length

Max length26
Median length26
Mean length24.539276
Min length16

Characters and Unicode

Total characters17553582
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLOBE Data Entry Web Forms
2nd rowGLOBE Data Entry Web Forms
3rd rowGLOBE Data Entry Web Forms
4th rowGLOBE Data Entry Web Forms
5th rowGLOBE Data Entry Web Forms

Common Values

ValueCountFrequency (%)
GLOBE Data Entry Web Forms 583099
81.4%
GLOBE Observer App 125752
 
17.6%
GLOBE Data Entry App 6468
 
0.9%
GLOBE EMDE SCOOL 7
 
< 0.1%
(Missing) 705
 
0.1%

Length

2025-07-07T15:02:08.136022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T15:02:08.158999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
globe 715326
21.6%
data 589567
17.8%
entry 589567
17.8%
web 583099
17.6%
forms 583099
17.6%
app 132220
 
4.0%
observer 125752
 
3.8%
emde 7
 
< 0.1%
scool 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2603318
14.8%
r 1424170
 
8.1%
E 1304907
 
7.4%
a 1179134
 
6.7%
t 1179134
 
6.7%
O 841092
 
4.8%
e 834603
 
4.8%
L 715333
 
4.1%
G 715326
 
4.1%
B 715326
 
4.1%
Other values (15) 6041239
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17553582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2603318
14.8%
r 1424170
 
8.1%
E 1304907
 
7.4%
a 1179134
 
6.7%
t 1179134
 
6.7%
O 841092
 
4.8%
e 834603
 
4.8%
L 715333
 
4.1%
G 715326
 
4.1%
B 715326
 
4.1%
Other values (15) 6041239
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17553582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2603318
14.8%
r 1424170
 
8.1%
E 1304907
 
7.4%
a 1179134
 
6.7%
t 1179134
 
6.7%
O 841092
 
4.8%
e 834603
 
4.8%
L 715333
 
4.1%
G 715326
 
4.1%
B 715326
 
4.1%
Other values (15) 6041239
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17553582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2603318
14.8%
r 1424170
 
8.1%
E 1304907
 
7.4%
a 1179134
 
6.7%
t 1179134
 
6.7%
O 841092
 
4.8%
e 834603
 
4.8%
L 715333
 
4.1%
G 715326
 
4.1%
B 715326
 
4.1%
Other values (15) 6041239
34.4%

developer_key_is_citizen_science
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing705
Missing (%)0.1%
Memory size1.4 MiB
False
589567 
True
125759 
(Missing)
 
705
ValueCountFrequency (%)
False 589567
82.3%
True 125759
 
17.6%
(Missing) 705
 
0.1%
2025-07-07T15:02:08.181009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-07-07T15:01:52.578994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:31.251468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:32.443557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:33.641838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:34.802962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:35.916451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:36.817069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:37.980920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:39.160911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:40.280873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:41.108033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:42.214252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:43.338209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:44.423898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T15:01:51.256562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.934187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T15:01:31.375084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T15:01:34.919658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-07T15:01:45.217323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:46.023819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:46.911441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:47.723538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:48.805359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:49.968404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.033501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.691242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:52.404225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:53.047224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:32.243852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:33.430445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:34.606774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:35.731779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:36.625537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:37.770848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:38.957394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:40.088922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:40.945632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:42.002075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:43.135395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:44.160242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:45.271621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:46.059807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:46.948057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:47.763634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:48.865980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:50.027201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.094178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.715106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:52.430884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:53.074072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:32.304834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:33.497266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:34.666699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:35.791442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:36.665311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:37.831768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:39.012278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:40.151532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:40.984298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:42.060104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:43.194765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:44.291522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:45.320633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:46.096711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:46.984813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:47.803231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:48.923725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:50.082738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.144108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.823679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:52.461451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:53.098949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:32.343438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:33.537120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:34.703933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:35.830621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:36.700742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:37.870140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:39.048510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:40.187484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:41.016754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:42.096421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:43.234706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:44.326133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:45.357184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:46.131877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:47.020830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:47.838886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:48.960232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:50.118716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.179232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.848606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:52.491022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:53.123538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:32.370360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:33.563044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:34.728878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:35.857099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:36.726669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:37.898440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:39.075628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:40.215582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:41.041875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:42.126243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:43.263745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:44.351353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:45.381235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:46.155867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:47.049700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:47.866858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:48.984555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:50.145564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.203477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.879328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:52.520616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:53.154804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:32.398259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:33.591144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:34.757805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:35.887353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:36.759116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:37.930027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:39.105873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:40.245304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:41.065815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:42.157565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:43.292645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:44.376598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:45.406149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:46.187432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:47.078391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:47.896178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:49.011704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:50.174519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.231640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:51.907332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-07T15:01:52.549741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-07T15:02:08.219245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
developer_key_is_citizen_sciencedeveloper_key_namehomogeneous_site_long_length_mhomogeneous_site_short_length_morganizationidsample_numbersample_snow_depth_flagsample_snow_depth_mmsample_surface_temperature_csite_developer_key_idsite_elevationsite_elevation_typesite_idsite_is_citizen_sciencesite_latitudesite_location_sourcesite_longitudesite_true_elevationsite_true_latitudesite_true_longitudesite_true_pointst_idsts_idsubmission_developer_key_idsubmission_elevationsubmission_idsubmission_latitudesubmission_longitudesurface_conditionsurface_cover_typeuser_type_descriptionuseridusertypeversionversion_id
developer_key_is_citizen_science1.0001.0000.4030.4270.3580.0090.2310.0630.1631.0000.0280.0580.4960.0220.3150.2910.2401.0001.0001.0001.0000.4070.4040.8020.0630.8070.2390.1670.0520.2980.2290.4600.2290.1790.657
developer_key_name1.0001.0000.3790.3850.2160.0080.1670.0450.0971.0000.0580.0580.3200.0220.1880.1760.1741.0001.0001.0001.0000.2420.2390.8170.0450.5880.1760.1550.0410.2290.1420.3000.1420.1030.388
homogeneous_site_long_length_m0.4030.3791.0000.796-0.200-0.0090.2380.069-0.2650.3790.2830.167-0.1011.0000.2620.196-0.272NaNNaNNaN0.000-0.194-0.1940.3540.0520.146-0.007-0.3010.1690.1690.354-0.2410.354-0.005-0.121
homogeneous_site_short_length_m0.4270.3850.7961.000-0.125-0.0300.2300.035-0.1660.3850.2830.130-0.0281.0000.0780.150-0.365NaNNaNNaN0.000-0.089-0.0890.3350.1570.1950.157-0.4700.1260.1190.243-0.1630.2430.068-0.018
organizationid0.3580.216-0.200-0.1251.000-0.0600.1420.0620.3880.216-0.2641.0000.6940.006-0.5140.1890.2990.180-0.817-0.5280.9310.5140.5140.508-0.1870.485-0.1950.2290.1170.2200.1970.7030.1970.2570.576
sample_number0.0090.008-0.009-0.030-0.0601.0000.0230.003-0.0590.0080.0100.044-0.0710.0000.0520.029-0.118-0.3190.079-0.0890.000-0.115-0.1150.101-0.112-0.0440.143-0.0720.0400.0850.136-0.1080.136-0.091-0.139
sample_snow_depth_flag0.2310.1670.2380.2300.1420.0231.0000.0000.1480.1670.0980.0850.2711.0000.1280.1520.1680.0000.0000.0000.0000.4160.4240.8120.1000.6050.2040.2140.3570.1960.4230.1810.4231.0000.214
sample_snow_depth_mm0.0630.0450.0690.0350.0620.0030.0001.000-0.5410.045-0.0090.0150.0841.0000.1500.0800.030NaNNaNNaN0.0000.1420.1410.2210.1300.4360.069-0.0590.2130.0320.0840.2710.0840.1120.113
sample_surface_temperature_c0.1630.097-0.265-0.1660.388-0.0590.148-0.5411.0000.097-0.3010.0940.2630.015-0.6020.0930.4120.1660.4240.2750.6720.3730.3730.461-0.0720.234-0.4280.1520.3920.0860.1980.4080.1980.1830.353
site_developer_key_id1.0001.0000.3790.3850.2160.0080.1670.0450.0971.0000.0580.0580.3200.0220.1880.1760.1741.0001.0001.0001.0000.2420.2390.8170.0450.5880.1760.1550.0410.2290.1420.3000.1420.1030.388
site_elevation0.0280.0580.2830.283-0.2640.0100.098-0.009-0.3010.0581.0000.186-0.2170.0100.1670.093-0.4881.0000.2060.2940.936-0.187-0.1870.2020.994-0.0970.189-0.4450.0950.1140.153-0.3270.1530.004-0.181
site_elevation_type0.0580.0580.1670.1301.0000.0440.0850.0150.0940.0580.1861.0001.0001.0000.1280.9260.1620.0000.0000.0000.0000.2200.2280.1681.0000.1671.0001.0000.0490.1340.2330.0490.2331.0000.104
site_id0.4960.320-0.101-0.0280.694-0.0710.2710.0840.2630.320-0.2171.0001.0000.043-0.2820.1970.148-0.152-0.489-0.8330.9420.6450.6450.883-0.1620.6320.1500.0950.1330.2390.3390.7280.3390.1390.714
site_is_citizen_science0.0220.0221.0001.0000.0060.0001.0001.0000.0150.0220.0101.0000.0431.0000.0100.0060.0200.7910.8270.9190.9310.0270.0230.0140.0000.0500.0320.0400.0071.0000.0080.0440.0081.0001.000
site_latitude0.3150.1880.2620.078-0.5140.0520.1280.150-0.6020.1880.1670.128-0.2820.0101.0000.128-0.3900.1170.9590.3750.936-0.302-0.3020.2850.178-0.2120.999-0.5650.1750.2580.247-0.3800.247-0.345-0.320
site_location_source0.2910.1760.1960.1500.1890.0290.1520.0800.0930.1760.0930.9260.1970.0060.1281.0000.1881.0001.0001.0001.0000.2180.2400.5100.0480.3000.1980.1000.0640.1220.1930.1960.1930.0220.124
site_longitude0.2400.174-0.272-0.3650.299-0.1180.1680.0300.4120.174-0.4880.1620.1480.020-0.3900.1881.0000.2940.4751.0000.9420.2700.2700.343-0.4360.055-0.5670.9990.1460.2720.3560.3570.3560.1480.273
site_true_elevation1.0001.000NaNNaN0.180-0.3190.000NaN0.1661.0001.0000.000-0.1520.7910.1171.0000.2941.0000.2060.2940.931-0.035-0.0351.0001.000-0.0350.2060.2940.0001.0000.077-0.4790.077NaNNaN
site_true_latitude1.0001.000NaNNaN-0.8170.0790.000NaN0.4241.0000.2060.000-0.4890.8270.9591.0000.4750.2061.0000.4750.948-0.799-0.7951.0000.207-0.7991.0000.4750.6721.0000.9820.0810.982NaNNaN
site_true_longitude1.0001.000NaNNaN-0.528-0.0890.000NaN0.2751.0000.2940.000-0.8330.9190.3751.0001.0000.2940.4751.0000.948-0.531-0.5281.0000.294-0.5310.4751.0000.3791.0000.937-0.2600.937NaNNaN
site_true_point1.0001.0000.0000.0000.9310.0000.0000.0000.6721.0000.9360.0000.9420.9310.9361.0000.9420.9310.9480.9481.0000.9360.9361.0000.9310.9420.9360.9480.9311.0000.9310.9540.9311.0001.000
st_id0.4070.242-0.194-0.0890.514-0.1150.4160.1420.3730.242-0.1870.2200.6450.027-0.3020.2180.270-0.035-0.799-0.5310.9361.0001.0001.0000.0311.0000.004-0.0270.1730.1450.5330.7860.5330.3430.901
sts_id0.4040.239-0.194-0.0890.514-0.1150.4240.1410.3730.239-0.1870.2280.6450.023-0.3020.2400.270-0.035-0.795-0.5280.9361.0001.0001.0000.0311.0000.004-0.0270.1850.1440.5590.7860.5590.3430.901
submission_developer_key_id0.8020.8170.3540.3350.5080.1010.8120.2210.4610.8170.2020.1680.8830.0140.2850.5100.3431.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.2130.3940.1640.7680.1640.1310.938
submission_elevation0.0630.0450.0520.157-0.187-0.1120.1000.130-0.0720.0450.9941.000-0.1620.0000.1780.048-0.4361.0000.2070.2940.9310.0310.0311.0001.0000.0310.178-0.4370.0240.1670.032-0.2930.0320.072-0.021
submission_id0.8070.5880.1460.1950.485-0.0440.6050.4360.2340.588-0.0970.1670.6320.050-0.2120.3000.055-0.035-0.799-0.5310.9421.0001.0001.0000.0311.0000.004-0.0270.1590.2680.2350.6470.2350.5520.908
submission_latitude0.2390.176-0.0070.157-0.1950.1430.2040.069-0.4280.1760.1891.0000.1500.0320.9990.198-0.5670.2061.0000.4750.9360.0040.0041.0000.1780.0041.000-0.5650.2450.3490.3420.0790.342-0.264-0.166
submission_longitude0.1670.155-0.301-0.4700.229-0.0720.214-0.0590.1520.155-0.4451.0000.0950.040-0.5650.1000.9990.2940.4751.0000.948-0.027-0.0271.000-0.437-0.027-0.5651.0000.1000.4090.3830.2310.3830.0040.048
surface_condition0.0520.0410.1690.1260.1170.0400.3570.2130.3920.0410.0950.0490.1330.0070.1750.0640.1460.0000.6720.3790.9310.1730.1850.2130.0240.1590.2450.1001.0000.0980.1340.1130.1340.0120.064
surface_cover_type0.2980.2290.1690.1190.2200.0850.1960.0320.0860.2290.1140.1340.2391.0000.2580.1220.2721.0001.0001.0001.0000.1450.1440.3940.1670.2680.3490.4090.0981.0000.2190.2020.2190.2700.247
user_type_description0.2290.1420.3540.2430.1970.1360.4230.0840.1980.1420.1530.2330.3390.0080.2470.1930.3560.0770.9820.9370.9310.5330.5590.1640.0320.2350.3420.3830.1340.2191.0000.2581.0000.0360.300
userid0.4600.300-0.241-0.1630.703-0.1080.1810.2710.4080.300-0.3270.0490.7280.044-0.3800.1960.357-0.4790.081-0.2600.9540.7860.7860.768-0.2930.6470.0790.2310.1130.2020.2581.0000.2580.2860.813
usertype0.2290.1420.3540.2430.1970.1360.4230.0840.1980.1420.1530.2330.3390.0080.2470.1930.3560.0770.9820.9370.9310.5330.5590.1640.0320.2350.3420.3830.1340.2191.0000.2581.0000.0360.300
version0.1790.103-0.0050.0680.257-0.0911.0000.1120.1830.1030.0041.0000.1391.000-0.3450.0220.148NaNNaNNaN1.0000.3430.3430.1310.0720.552-0.2640.0040.0120.2700.0360.2860.0361.0000.404
version_id0.6570.388-0.121-0.0180.576-0.1390.2140.1130.3530.388-0.1810.1040.7141.000-0.3200.1240.273NaNNaNNaN1.0000.9010.9010.938-0.0210.908-0.1660.0480.0640.2470.3000.8130.3000.4041.000

Missing values

2025-07-07T15:01:53.524745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-07T15:01:55.054060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-07T15:01:59.717565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

st_idsite_idmeasured_atprotocol_iduseridsurface_conditionorganizationidusertypesubmission_idst_updated_atst_created_atsts_idsample_numbersample_surface_temperature_csample_snow_depth_mmsample_snow_depth_flagversion_idversionsite_version_activated_atversion_datesite_version_commentshomogeneous_site_short_length_mhomogeneous_site_long_length_msurface_cover_typeinstrument_typeprotocol_nameprotocol_modelprotocol_association_nameprotocol_alt_nameprotocol_investigation_areauser_type_descriptionsubmission_commentssubmission_developer_key_idsubmission_access_code_idsubmission_latitudesubmission_longitudesubmission_elevationsubmission_pointsubmission_dataprotocol_set_nameprotocol_set_codesite_namesite_activated_atsite_deactivated_atsite_commentssite_latitudesite_longitudesite_elevationsite_elevation_typesite_location_sourcesite_pointsite_developer_key_idsite_is_citizen_sciencesite_nicknamesite_true_latitudesite_true_longitudesite_true_elevationsite_true_pointsite_photo_measured_atsite_photo_primary_thumb_urlsite_photo_primary_photo_urlsite_photo_photo_datadeveloper_key_namedeveloper_key_is_citizen_science
01957106522008-01-21 11:00:008-1None166361-1<NA>2012-07-03 13:56:42.7224292012-07-03 13:56:42.7224161295216.50.0<NA>724812008-01-21 11:00:002008-02-05 09:20:51please replace with Surface Temperature Site Comments30.030.0short grassraytek st20Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Gim7RZ/PL/143:ATM1892008-01-31NaT<NA>50.146622.1738179.1ellipsoidalgps01010000A0E6100000780B24287E2C3640FBCBEEC9C312494033333333336366401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse
11957106522008-01-21 11:00:008-1None166361-1<NA>2012-07-03 13:56:42.7224292012-07-03 13:56:42.7224161295926.00.0<NA>724812008-01-21 11:00:002008-02-05 09:20:51please replace with Surface Temperature Site Comments30.030.0short grassraytek st20Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Gim7RZ/PL/143:ATM1892008-01-31NaT<NA>50.146622.1738179.1ellipsoidalgps01010000A0E6100000780B24287E2C3640FBCBEEC9C312494033333333336366401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse
21957106522008-01-21 11:00:008-1None166361-1<NA>2012-07-03 13:56:42.7224292012-07-03 13:56:42.7224161296636.50.0<NA>724812008-01-21 11:00:002008-02-05 09:20:51please replace with Surface Temperature Site Comments30.030.0short grassraytek st20Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Gim7RZ/PL/143:ATM1892008-01-31NaT<NA>50.146622.1738179.1ellipsoidalgps01010000A0E6100000780B24287E2C3640FBCBEEC9C312494033333333336366401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse
31958106522008-01-22 11:00:008-1None166361-1<NA>2012-07-03 13:56:42.7439042012-07-03 13:56:42.7438911295316.00.0<NA>724812008-01-21 11:00:002008-02-05 09:20:51please replace with Surface Temperature Site Comments30.030.0short grassraytek st20Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Gim7RZ/PL/143:ATM1892008-01-31NaT<NA>50.146622.1738179.1ellipsoidalgps01010000A0E6100000780B24287E2C3640FBCBEEC9C312494033333333336366401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse
41958106522008-01-22 11:00:008-1None166361-1<NA>2012-07-03 13:56:42.7439042012-07-03 13:56:42.7438911296026.00.0<NA>724812008-01-21 11:00:002008-02-05 09:20:51please replace with Surface Temperature Site Comments30.030.0short grassraytek st20Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Gim7RZ/PL/143:ATM1892008-01-31NaT<NA>50.146622.1738179.1ellipsoidalgps01010000A0E6100000780B24287E2C3640FBCBEEC9C312494033333333336366401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse
51958106522008-01-22 11:00:008-1None166361-1<NA>2012-07-03 13:56:42.7439042012-07-03 13:56:42.7438911296736.00.0<NA>724812008-01-21 11:00:002008-02-05 09:20:51please replace with Surface Temperature Site Comments30.030.0short grassraytek st20Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Gim7RZ/PL/143:ATM1892008-01-31NaT<NA>50.146622.1738179.1ellipsoidalgps01010000A0E6100000780B24287E2C3640FBCBEEC9C312494033333333336366401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse
61959106522008-01-23 11:00:008-1None166361-1<NA>2012-07-03 13:56:42.7595352012-07-03 13:56:42.7595221295410.00.0<NA>724812008-01-21 11:00:002008-02-05 09:20:51please replace with Surface Temperature Site Comments30.030.0short grassraytek st20Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Gim7RZ/PL/143:ATM1892008-01-31NaT<NA>50.146622.1738179.1ellipsoidalgps01010000A0E6100000780B24287E2C3640FBCBEEC9C312494033333333336366401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse
71959106522008-01-23 11:00:008-1None166361-1<NA>2012-07-03 13:56:42.7595352012-07-03 13:56:42.7595221296120.00.0<NA>724812008-01-21 11:00:002008-02-05 09:20:51please replace with Surface Temperature Site Comments30.030.0short grassraytek st20Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Gim7RZ/PL/143:ATM1892008-01-31NaT<NA>50.146622.1738179.1ellipsoidalgps01010000A0E6100000780B24287E2C3640FBCBEEC9C312494033333333336366401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse
81959106522008-01-23 11:00:008-1None166361-1<NA>2012-07-03 13:56:42.7595352012-07-03 13:56:42.7595221296830.00.0<NA>724812008-01-21 11:00:002008-02-05 09:20:51please replace with Surface Temperature Site Comments30.030.0short grassraytek st20Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Gim7RZ/PL/143:ATM1892008-01-31NaT<NA>50.146622.1738179.1ellipsoidalgps01010000A0E6100000780B24287E2C3640FBCBEEC9C312494033333333336366401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse
91960106522008-01-24 11:00:008-1None166361-1<NA>2012-07-03 13:56:42.7654812012-07-03 13:56:42.7654611295510.50.0<NA>724812008-01-21 11:00:002008-02-05 09:20:51please replace with Surface Temperature Site Comments30.030.0short grassraytek st20Surface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenot categorized<NA><NA><NA>NaNNaNNaNNoneNone<NA><NA>Gim7RZ/PL/143:ATM1892008-01-31NaT<NA>50.146622.1738179.1ellipsoidalgps01010000A0E6100000780B24287E2C3640FBCBEEC9C312494033333333336366401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse
st_idsite_idmeasured_atprotocol_iduseridsurface_conditionorganizationidusertypesubmission_idst_updated_atst_created_atsts_idsample_numbersample_surface_temperature_csample_snow_depth_mmsample_snow_depth_flagversion_idversionsite_version_activated_atversion_datesite_version_commentshomogeneous_site_short_length_mhomogeneous_site_long_length_msurface_cover_typeinstrument_typeprotocol_nameprotocol_modelprotocol_association_nameprotocol_alt_nameprotocol_investigation_areauser_type_descriptionsubmission_commentssubmission_developer_key_idsubmission_access_code_idsubmission_latitudesubmission_longitudesubmission_elevationsubmission_pointsubmission_dataprotocol_set_nameprotocol_set_codesite_namesite_activated_atsite_deactivated_atsite_commentssite_latitudesite_longitudesite_elevationsite_elevation_typesite_location_sourcesite_pointsite_developer_key_idsite_is_citizen_sciencesite_nicknamesite_true_latitudesite_true_longitudesite_true_elevationsite_true_pointsite_photo_measured_atsite_photo_primary_thumb_urlsite_photo_primary_photo_urlsite_photo_photo_datadeveloper_key_namedeveloper_key_is_citizen_science
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7160301669693181802025-03-27 17:40:008107437128dry10743716111599280752025-03-27 18:13:17.1131692025-03-27 18:13:17.113169729237932.0NaN<NA>3385312023-06-14 18:39:37.7943282023-06-14 18:39:37.794322<NA>53.050.0asphaltEtekcitySurface TemperatureSurfaceTemperaturesurface_temperatureSurface TemperatureAtmospherenon-student user - trained<NA>5<NA>40.117903-83.160308279.501010000A0E610000061527C7C42CA54C0D68C0C72170F44400000000000787140NoneSurface Temperature9808asphalt parking lot2023-06-14NaTAsphalt Parking40.117903-83.160308279.5<NA>other01010000A0E610000061527C7C42CA54C0D68C0C72170F444000000000007871401False<NA>NaNNaNNaNNoneNaT<NA><NA><NA>GLOBE Data Entry Web FormsFalse